"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Display a few random images from the training set\n",
+ "plt.figure(figsize=(10,10))\n",
+ "for i in range(9):\n",
+ " plt.subplot(3, 3, i+1)\n",
+ " plt.imshow(X_train[i])\n",
+ " plt.title(f\"Class: {y_train[i].argmax()}\")\n",
+ " plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Model Architecture"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"sequential\"\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ conv2d (Conv2D) │ (None, 32, 32, 32) │ 896 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization │ (None, 32, 32, 32) │ 128 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_1 (Conv2D) │ (None, 32, 32, 32) │ 9,248 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_1 │ (None, 32, 32, 32) │ 128 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d (MaxPooling2D) │ (None, 16, 16, 32) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout (Dropout) │ (None, 16, 16, 32) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_2 (Conv2D) │ (None, 16, 16, 64) │ 18,496 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_2 │ (None, 16, 16, 64) │ 256 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_3 (Conv2D) │ (None, 16, 16, 64) │ 36,928 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_3 │ (None, 16, 16, 64) │ 256 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_1 (MaxPooling2D) │ (None, 8, 8, 64) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_1 (Dropout) │ (None, 8, 8, 64) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_4 (Conv2D) │ (None, 8, 8, 128) │ 73,856 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_4 │ (None, 8, 8, 128) │ 512 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_5 (Conv2D) │ (None, 8, 8, 128) │ 147,584 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_5 │ (None, 8, 8, 128) │ 512 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_2 (MaxPooling2D) │ (None, 4, 4, 128) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_2 (Dropout) │ (None, 4, 4, 128) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_6 (Conv2D) │ (None, 4, 4, 256) │ 295,168 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_6 │ (None, 4, 4, 256) │ 1,024 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_7 (Conv2D) │ (None, 4, 4, 256) │ 590,080 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_7 │ (None, 4, 4, 256) │ 1,024 │\n",
+ "│ (BatchNormalization) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_3 (MaxPooling2D) │ (None, 2, 2, 256) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_3 (Dropout) │ (None, 2, 2, 256) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ flatten (Flatten) │ (None, 1024) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense (Dense) │ (None, 256) │ 262,400 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_4 (Dropout) │ (None, 256) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_1 (Dense) │ (None, 10) │ 2,570 │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m262,400\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m2,570\u001b[0m │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 1,441,066 (5.50 MB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,441,066\u001b[0m (5.50 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 1,439,146 (5.49 MB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,439,146\u001b[0m (5.49 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 1,920 (7.50 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,920\u001b[0m (7.50 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Build a Convolutional Neural Network (CNN) suitable for image classification.\n",
+ "\n",
+ "# Model / data parameters\n",
+ "num_classes = 10\n",
+ "input_shape = (32, 32, 3)\n",
+ "\n",
+ "# Build the CNN with convolutional blocks and poolings (flatten, dropout and dense at the end to fully connect layers)\n",
+ "model = keras.Sequential(\n",
+ " [\n",
+ " keras.Input(shape=input_shape),\n",
+ " layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\", padding='same'),\n",
+ " layers.BatchNormalization(), # Adding batch normalization after each convolutional block can help stabilize and faster the training.\n",
+ " layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\", padding='same'),\n",
+ " layers.BatchNormalization(),\n",
+ " layers.MaxPooling2D(pool_size=(2, 2)),\n",
+ " layers.Dropout(0.25), # Add dropout to reduce overfitting\n",
+ "\n",
+ " layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\", padding='same'),\n",
+ " layers.BatchNormalization(),\n",
+ " layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\", padding='same'),\n",
+ " layers.BatchNormalization(),\n",
+ " layers.MaxPooling2D(pool_size=(2, 2)),\n",
+ " layers.Dropout(0.25),\n",
+ "\n",
+ " layers.Conv2D(128, kernel_size=(3, 3), activation=\"relu\", padding='same'),\n",
+ " layers.BatchNormalization(),\n",
+ " layers.Conv2D(128, kernel_size=(3, 3), activation=\"relu\", padding='same'),\n",
+ " layers.BatchNormalization(),\n",
+ " layers.MaxPooling2D(pool_size=(2, 2)),\n",
+ " layers.Dropout(0.3),\n",
+ "\n",
+ " layers.Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same'),\n",
+ " layers.BatchNormalization(),\n",
+ " layers.Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same'),\n",
+ " layers.BatchNormalization(),\n",
+ " layers.MaxPooling2D(pool_size=(2, 2)),\n",
+ " layers.Dropout(0.4),\n",
+ " \n",
+ " layers.Flatten(),\n",
+ " layers.Dense(256, activation=\"relu\"), # Add a fully connected layer before the output\n",
+ " layers.Dropout(0.5), # Increase dropout for the fully connected layer\n",
+ " layers.Dense(num_classes, activation=\"softmax\"),\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 3. Model Training"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 102ms/step - accuracy: 0.1634 - loss: 3.1818 - val_accuracy: 0.0913 - val_loss: 3.4351\n",
+ "Epoch 2/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 101ms/step - accuracy: 0.2555 - loss: 2.0175 - val_accuracy: 0.1412 - val_loss: 3.1301\n",
+ "Epoch 3/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 108ms/step - accuracy: 0.3142 - loss: 1.8409 - val_accuracy: 0.3313 - val_loss: 1.8434\n",
+ "Epoch 4/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 92ms/step - accuracy: 0.3560 - loss: 1.7441 - val_accuracy: 0.4025 - val_loss: 1.6365\n",
+ "Epoch 5/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 125ms/step - accuracy: 0.3938 - loss: 1.6447 - val_accuracy: 0.4025 - val_loss: 1.5981\n",
+ "Epoch 6/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 95ms/step - accuracy: 0.4255 - loss: 1.5691 - val_accuracy: 0.4363 - val_loss: 1.5140\n",
+ "Epoch 7/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 98ms/step - accuracy: 0.4540 - loss: 1.5071 - val_accuracy: 0.4688 - val_loss: 1.4918\n",
+ "Epoch 8/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 100ms/step - accuracy: 0.4607 - loss: 1.4495 - val_accuracy: 0.5138 - val_loss: 1.3021\n",
+ "Epoch 9/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 95ms/step - accuracy: 0.4854 - loss: 1.4026 - val_accuracy: 0.4913 - val_loss: 1.3647\n",
+ "Epoch 10/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 97ms/step - accuracy: 0.5175 - loss: 1.3552 - val_accuracy: 0.5188 - val_loss: 1.3848\n",
+ "Epoch 11/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 91ms/step - accuracy: 0.5517 - loss: 1.2666 - val_accuracy: 0.5312 - val_loss: 1.3352\n",
+ "Epoch 12/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 150ms/step - accuracy: 0.5568 - loss: 1.2408 - val_accuracy: 0.5562 - val_loss: 1.2045\n",
+ "Epoch 13/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 125ms/step - accuracy: 0.5706 - loss: 1.1864 - val_accuracy: 0.5750 - val_loss: 1.1476\n",
+ "Epoch 14/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 96ms/step - accuracy: 0.6048 - loss: 1.1239 - val_accuracy: 0.5913 - val_loss: 1.1581\n",
+ "Epoch 15/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 104ms/step - accuracy: 0.6132 - loss: 1.0889 - val_accuracy: 0.5487 - val_loss: 1.3422\n",
+ "Epoch 16/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 114ms/step - accuracy: 0.6198 - loss: 1.0502 - val_accuracy: 0.6075 - val_loss: 1.1868\n",
+ "Epoch 17/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 102ms/step - accuracy: 0.6342 - loss: 0.9997 - val_accuracy: 0.6463 - val_loss: 1.0080\n",
+ "Epoch 18/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 105ms/step - accuracy: 0.6635 - loss: 0.9612 - val_accuracy: 0.6550 - val_loss: 1.0104\n",
+ "Epoch 19/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 117ms/step - accuracy: 0.6789 - loss: 0.8990 - val_accuracy: 0.6525 - val_loss: 1.0384\n",
+ "Epoch 20/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 112ms/step - accuracy: 0.6864 - loss: 0.8749 - val_accuracy: 0.6712 - val_loss: 1.0001\n",
+ "Epoch 21/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 104ms/step - accuracy: 0.6973 - loss: 0.8232 - val_accuracy: 0.6263 - val_loss: 1.0707\n",
+ "Epoch 22/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 98ms/step - accuracy: 0.7191 - loss: 0.7978 - val_accuracy: 0.6400 - val_loss: 1.1208\n",
+ "Epoch 23/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 104ms/step - accuracy: 0.7264 - loss: 0.7606 - val_accuracy: 0.6675 - val_loss: 1.0353\n",
+ "Epoch 24/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 97ms/step - accuracy: 0.7396 - loss: 0.7263 - val_accuracy: 0.6637 - val_loss: 1.0819\n",
+ "Epoch 25/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 121ms/step - accuracy: 0.7566 - loss: 0.6769 - val_accuracy: 0.6938 - val_loss: 0.9132\n",
+ "Epoch 26/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 102ms/step - accuracy: 0.7595 - loss: 0.6702 - val_accuracy: 0.6875 - val_loss: 0.9672\n",
+ "Epoch 27/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 95ms/step - accuracy: 0.7760 - loss: 0.6254 - val_accuracy: 0.6850 - val_loss: 1.0099\n",
+ "Epoch 28/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 105ms/step - accuracy: 0.7989 - loss: 0.5833 - val_accuracy: 0.7000 - val_loss: 0.9881\n",
+ "Epoch 29/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 99ms/step - accuracy: 0.8031 - loss: 0.5540 - val_accuracy: 0.6938 - val_loss: 1.0394\n",
+ "Epoch 30/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 101ms/step - accuracy: 0.8219 - loss: 0.5162 - val_accuracy: 0.6938 - val_loss: 0.9626\n",
+ "Epoch 31/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 92ms/step - accuracy: 0.8195 - loss: 0.5144 - val_accuracy: 0.6787 - val_loss: 1.0782\n",
+ "Epoch 32/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 91ms/step - accuracy: 0.8485 - loss: 0.4451 - val_accuracy: 0.6925 - val_loss: 1.1083\n",
+ "Epoch 33/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 104ms/step - accuracy: 0.8369 - loss: 0.4743 - val_accuracy: 0.7200 - val_loss: 0.9747\n",
+ "Epoch 34/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 96ms/step - accuracy: 0.8490 - loss: 0.4401 - val_accuracy: 0.7212 - val_loss: 0.9917\n",
+ "Epoch 35/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 104ms/step - accuracy: 0.8715 - loss: 0.3663 - val_accuracy: 0.7200 - val_loss: 1.0984\n",
+ "Epoch 36/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 101ms/step - accuracy: 0.8761 - loss: 0.3647 - val_accuracy: 0.7100 - val_loss: 1.1047\n",
+ "Epoch 37/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 92ms/step - accuracy: 0.8859 - loss: 0.3358 - val_accuracy: 0.6988 - val_loss: 1.2390\n",
+ "Epoch 38/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 98ms/step - accuracy: 0.8771 - loss: 0.3425 - val_accuracy: 0.7212 - val_loss: 1.1052\n",
+ "Epoch 39/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 97ms/step - accuracy: 0.8931 - loss: 0.3183 - val_accuracy: 0.7325 - val_loss: 1.0453\n",
+ "Epoch 40/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 90ms/step - accuracy: 0.9024 - loss: 0.2808 - val_accuracy: 0.7038 - val_loss: 1.1520\n",
+ "Epoch 41/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 88ms/step - accuracy: 0.9089 - loss: 0.2644 - val_accuracy: 0.7275 - val_loss: 1.0937\n",
+ "Epoch 42/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 90ms/step - accuracy: 0.9117 - loss: 0.2413 - val_accuracy: 0.7200 - val_loss: 1.1246\n",
+ "Epoch 43/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 92ms/step - accuracy: 0.9123 - loss: 0.2610 - val_accuracy: 0.7200 - val_loss: 1.1119\n",
+ "Epoch 44/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 104ms/step - accuracy: 0.9049 - loss: 0.2878 - val_accuracy: 0.7350 - val_loss: 1.1812\n",
+ "Epoch 45/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 96ms/step - accuracy: 0.9114 - loss: 0.2589 - val_accuracy: 0.7212 - val_loss: 1.1984\n",
+ "Epoch 46/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 93ms/step - accuracy: 0.9210 - loss: 0.2272 - val_accuracy: 0.7350 - val_loss: 1.1037\n",
+ "Epoch 47/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 120ms/step - accuracy: 0.9242 - loss: 0.2266 - val_accuracy: 0.7038 - val_loss: 1.3154\n",
+ "Epoch 48/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 122ms/step - accuracy: 0.9268 - loss: 0.2170 - val_accuracy: 0.7163 - val_loss: 1.2234\n",
+ "Epoch 49/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 95ms/step - accuracy: 0.9359 - loss: 0.1849 - val_accuracy: 0.7275 - val_loss: 1.2090\n",
+ "Epoch 50/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 91ms/step - accuracy: 0.9440 - loss: 0.1702 - val_accuracy: 0.7013 - val_loss: 1.2519\n",
+ "Epoch 51/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 93ms/step - accuracy: 0.9387 - loss: 0.1884 - val_accuracy: 0.7300 - val_loss: 1.1844\n",
+ "Epoch 52/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 95ms/step - accuracy: 0.9453 - loss: 0.1674 - val_accuracy: 0.7188 - val_loss: 1.4200\n",
+ "Epoch 53/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 94ms/step - accuracy: 0.9399 - loss: 0.1796 - val_accuracy: 0.7275 - val_loss: 1.2962\n",
+ "Epoch 54/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 104ms/step - accuracy: 0.9357 - loss: 0.1907 - val_accuracy: 0.7450 - val_loss: 1.2141\n",
+ "Epoch 55/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 95ms/step - accuracy: 0.9440 - loss: 0.1639 - val_accuracy: 0.7138 - val_loss: 1.3019\n",
+ "Epoch 56/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 98ms/step - accuracy: 0.9435 - loss: 0.1731 - val_accuracy: 0.7450 - val_loss: 1.2492\n",
+ "Epoch 57/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 97ms/step - accuracy: 0.9495 - loss: 0.1455 - val_accuracy: 0.7237 - val_loss: 1.2745\n",
+ "Epoch 58/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 93ms/step - accuracy: 0.9532 - loss: 0.1449 - val_accuracy: 0.7225 - val_loss: 1.2370\n",
+ "Epoch 59/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 91ms/step - accuracy: 0.9463 - loss: 0.1529 - val_accuracy: 0.7387 - val_loss: 1.2186\n",
+ "Epoch 60/60\n",
+ "\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 99ms/step - accuracy: 0.9521 - loss: 0.1442 - val_accuracy: 0.7250 - val_loss: 1.2735\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compile the model\n",
+ "model.compile(loss=\"categorical_crossentropy\", optimizer=Adam(learning_rate=0.0003), metrics=[\"accuracy\"])\n",
+ "\n",
+ "# Train the model\n",
+ "history_cnn = model.fit(X_train[:8000], y_train[:8000], batch_size=32, epochs=60, validation_split=0.1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Function to plot the training and validation accuracy and loss\n",
+ "def plot_accuracy_and_loss(history, model_name):\n",
+ " # Plot accuracy\n",
+ " plt.figure(figsize=(14, 5))\n",
+ "\n",
+ " # Plot Accuracy\n",
+ " plt.subplot(1, 2, 1)\n",
+ " plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
+ " plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
+ " plt.title(f'{model_name} - Accuracy')\n",
+ " plt.xlabel('Epochs')\n",
+ " plt.ylabel('Accuracy')\n",
+ " plt.legend()\n",
+ "\n",
+ " # Plot Loss\n",
+ " plt.subplot(1, 2, 2)\n",
+ " plt.plot(history.history['loss'], label='Train Loss')\n",
+ " plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+ " plt.title(f'{model_name} - Loss')\n",
+ " plt.xlabel('Epochs')\n",
+ " plt.ylabel('Loss')\n",
+ " plt.legend()\n",
+ "\n",
+ " plt.show()\n",
+ "\n",
+ "# Plot accuracy and loss for the Custom CNN model\n",
+ "plot_accuracy_and_loss(history_cnn, 'CNN Model')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 4. Model Evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# List of CIFAR-10 class names\n",
+ "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test loss: 1.3723511695861816\n",
+ "Test accuracy: 0.7222999930381775\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compute and report metrics \n",
+ "score = model.evaluate(X_test, y_test, verbose=3)\n",
+ "print(\"Test loss:\", score[0])\n",
+ "print(\"Test accuracy:\", score[1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 31ms/step\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get predictions for the test set\n",
+ "y_pred = model.predict(X_test)\n",
+ "y_pred_classes = np.argmax(y_pred, axis=1) # Convert predicted probabilities to class labels\n",
+ "y_true = np.argmax(y_test, axis=1) # Convert one-hot encoded labels to class labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Compute the confusion matrix\n",
+ "conf_matrix = confusion_matrix(y_true, y_pred_classes)\n",
+ "\n",
+ "# Visualize the confusion matrix to understand model performance across different classes.\n",
+ "plt.figure(figsize=(10, 8))\n",
+ "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
+ "plt.xlabel('Predicted Labels')\n",
+ "plt.ylabel('True Labels')\n",
+ "plt.title('Confusion Matrix for CIFAR-10')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Save Model as pickle file\n",
+ "import pickle\n",
+ "with open('CNN_Charlie_Dani.pkl', 'wb') as f:\n",
+ " pickle.dump(model, f)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 5. Transfer Learning (with VGG16)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load VGG16 model pre-trained on ImageNet, excluding the top layers\n",
+ "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(32, 32, 3))\n",
+ "\n",
+ "# Freeze the base model layers\n",
+ "for layer in base_model.layers:\n",
+ " layer.trainable = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Add GlobalAveragePooling2D layer to flatten the output of VGG16\n",
+ "avg = GlobalAveragePooling2D()(base_model.output)\n",
+ "\n",
+ "# Add a fully connected layer for classification\n",
+ "output = layers.Dense(num_classes, activation='softmax')(avg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Model: \"functional_2\"\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"functional_2\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ input_layer_3 (InputLayer) │ (None, 32, 32, 3) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block1_conv1 (Conv2D) │ (None, 32, 32, 64) │ 1,792 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block1_conv2 (Conv2D) │ (None, 32, 32, 64) │ 36,928 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block1_pool (MaxPooling2D) │ (None, 16, 16, 64) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block2_conv1 (Conv2D) │ (None, 16, 16, 128) │ 73,856 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block2_conv2 (Conv2D) │ (None, 16, 16, 128) │ 147,584 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block2_pool (MaxPooling2D) │ (None, 8, 8, 128) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block3_conv1 (Conv2D) │ (None, 8, 8, 256) │ 295,168 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block3_conv2 (Conv2D) │ (None, 8, 8, 256) │ 590,080 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block3_conv3 (Conv2D) │ (None, 8, 8, 256) │ 590,080 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block3_pool (MaxPooling2D) │ (None, 4, 4, 256) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block4_conv1 (Conv2D) │ (None, 4, 4, 512) │ 1,180,160 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block4_conv2 (Conv2D) │ (None, 4, 4, 512) │ 2,359,808 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block4_conv3 (Conv2D) │ (None, 4, 4, 512) │ 2,359,808 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block4_pool (MaxPooling2D) │ (None, 2, 2, 512) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block5_conv1 (Conv2D) │ (None, 2, 2, 512) │ 2,359,808 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block5_conv2 (Conv2D) │ (None, 2, 2, 512) │ 2,359,808 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block5_conv3 (Conv2D) │ (None, 2, 2, 512) │ 2,359,808 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block5_pool (MaxPooling2D) │ (None, 1, 1, 512) │ 0 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling2d │ (None, 512) │ 0 │\n",
+ "│ (GlobalAveragePooling2D) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_3 (Dense) │ (None, 10) │ 5,130 │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ input_layer_3 (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m1,792\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,180,160\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,359,808\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ global_average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m5,130\u001b[0m │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 14,719,818 (56.15 MB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m14,719,818\u001b[0m (56.15 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Trainable params: 5,130 (20.04 KB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m5,130\u001b[0m (20.04 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Non-trainable params: 14,714,688 (56.13 MB)\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m14,714,688\u001b[0m (56.13 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Add new fully connected layers on top of VGG16 for CIFAR-10 (10 classes)\n",
+ "combined_model = models.Model(inputs=base_model.input, outputs=output)\n",
+ "\n",
+ "# Summary of the model\n",
+ "combined_model.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 90ms/step - accuracy: 0.2905 - loss: 2.0194 - val_accuracy: 0.4898 - val_loss: 1.5449\n",
+ "Epoch 2/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 98ms/step - accuracy: 0.4860 - loss: 1.5316 - val_accuracy: 0.5274 - val_loss: 1.4182\n",
+ "Epoch 3/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 100ms/step - accuracy: 0.5189 - loss: 1.4234 - val_accuracy: 0.5426 - val_loss: 1.3590\n",
+ "Epoch 4/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 91ms/step - accuracy: 0.5369 - loss: 1.3649 - val_accuracy: 0.5546 - val_loss: 1.3213\n",
+ "Epoch 5/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m129s\u001b[0m 91ms/step - accuracy: 0.5449 - loss: 1.3316 - val_accuracy: 0.5526 - val_loss: 1.2946\n",
+ "Epoch 6/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m124s\u001b[0m 88ms/step - accuracy: 0.5636 - loss: 1.2939 - val_accuracy: 0.5624 - val_loss: 1.2787\n",
+ "Epoch 7/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 90ms/step - accuracy: 0.5690 - loss: 1.2731 - val_accuracy: 0.5670 - val_loss: 1.2601\n",
+ "Epoch 8/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m128s\u001b[0m 91ms/step - accuracy: 0.5722 - loss: 1.2564 - val_accuracy: 0.5694 - val_loss: 1.2469\n",
+ "Epoch 9/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 102ms/step - accuracy: 0.5734 - loss: 1.2463 - val_accuracy: 0.5748 - val_loss: 1.2406\n",
+ "Epoch 10/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 103ms/step - accuracy: 0.5833 - loss: 1.2270 - val_accuracy: 0.5798 - val_loss: 1.2266\n",
+ "Epoch 11/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 99ms/step - accuracy: 0.5823 - loss: 1.2303 - val_accuracy: 0.5846 - val_loss: 1.2191\n",
+ "Epoch 12/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 98ms/step - accuracy: 0.5853 - loss: 1.2182 - val_accuracy: 0.5830 - val_loss: 1.2124\n",
+ "Epoch 13/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m214s\u001b[0m 152ms/step - accuracy: 0.5845 - loss: 1.2175 - val_accuracy: 0.5834 - val_loss: 1.2075\n",
+ "Epoch 14/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 99ms/step - accuracy: 0.5876 - loss: 1.2042 - val_accuracy: 0.5852 - val_loss: 1.2035\n",
+ "Epoch 15/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 104ms/step - accuracy: 0.5942 - loss: 1.1900 - val_accuracy: 0.5858 - val_loss: 1.1990\n",
+ "Epoch 16/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 104ms/step - accuracy: 0.5935 - loss: 1.1902 - val_accuracy: 0.5894 - val_loss: 1.1927\n",
+ "Epoch 17/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 99ms/step - accuracy: 0.5941 - loss: 1.1816 - val_accuracy: 0.5922 - val_loss: 1.1886\n",
+ "Epoch 18/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 98ms/step - accuracy: 0.5967 - loss: 1.1778 - val_accuracy: 0.5884 - val_loss: 1.1890\n",
+ "Epoch 19/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47461s\u001b[0m 34s/step - accuracy: 0.5975 - loss: 1.1733 - val_accuracy: 0.5922 - val_loss: 1.1856\n",
+ "Epoch 20/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m334s\u001b[0m 237ms/step - accuracy: 0.5986 - loss: 1.1699 - val_accuracy: 0.5944 - val_loss: 1.1816\n",
+ "Epoch 21/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 132ms/step - accuracy: 0.5989 - loss: 1.1687 - val_accuracy: 0.5936 - val_loss: 1.1786\n",
+ "Epoch 22/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m230s\u001b[0m 164ms/step - accuracy: 0.5993 - loss: 1.1681 - val_accuracy: 0.5940 - val_loss: 1.1777\n",
+ "Epoch 23/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m197s\u001b[0m 140ms/step - accuracy: 0.5983 - loss: 1.1680 - val_accuracy: 0.5962 - val_loss: 1.1755\n",
+ "Epoch 24/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m223s\u001b[0m 159ms/step - accuracy: 0.6035 - loss: 1.1560 - val_accuracy: 0.5982 - val_loss: 1.1737\n",
+ "Epoch 25/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m224s\u001b[0m 159ms/step - accuracy: 0.6083 - loss: 1.1488 - val_accuracy: 0.5944 - val_loss: 1.1706\n",
+ "Epoch 26/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m221s\u001b[0m 157ms/step - accuracy: 0.6008 - loss: 1.1580 - val_accuracy: 0.5966 - val_loss: 1.1708\n",
+ "Epoch 27/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m240s\u001b[0m 170ms/step - accuracy: 0.6034 - loss: 1.1519 - val_accuracy: 0.5956 - val_loss: 1.1696\n",
+ "Epoch 28/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m234s\u001b[0m 166ms/step - accuracy: 0.6109 - loss: 1.1392 - val_accuracy: 0.5944 - val_loss: 1.1702\n",
+ "Epoch 29/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 173ms/step - accuracy: 0.6084 - loss: 1.1422 - val_accuracy: 0.5958 - val_loss: 1.1662\n",
+ "Epoch 30/30\n",
+ "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m230s\u001b[0m 164ms/step - accuracy: 0.6070 - loss: 1.1443 - val_accuracy: 0.5980 - val_loss: 1.1637\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compile the model\n",
+ "combined_model.compile(optimizer=Adam(learning_rate=0.0003), loss='categorical_crossentropy', metrics=['accuracy'])\n",
+ "\n",
+ "# Train the model using the CIFAR-10 dataset\n",
+ "history_vgg16 = combined_model.fit(X_train, y_train, epochs=30, batch_size=32, validation_split=0.1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "\u001b[1m450/450\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 190ms/step - accuracy: 0.5113 - loss: 1.4282 - val_accuracy: 0.6062 - val_loss: 1.0947\n",
+ "Epoch 2/10\n",
+ "\u001b[1m450/450\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 193ms/step - accuracy: 0.6924 - loss: 0.8597 - val_accuracy: 0.6175 - val_loss: 1.0790\n",
+ "Epoch 3/10\n",
+ "\u001b[1m450/450\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 197ms/step - accuracy: 0.7761 - loss: 0.6299 - val_accuracy: 0.6600 - val_loss: 1.0444\n",
+ "Epoch 4/10\n",
+ "\u001b[1m450/450\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 179ms/step - accuracy: 0.8586 - loss: 0.4054 - val_accuracy: 0.6538 - val_loss: 1.0990\n",
+ "Epoch 5/10\n",
+ "\u001b[1m450/450\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 169ms/step - accuracy: 0.8999 - loss: 0.2837 - val_accuracy: 0.6325 - val_loss: 1.2970\n",
+ "Epoch 6/10\n",
+ "\u001b[1m450/450\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m88s\u001b[0m 196ms/step - accuracy: 0.9345 - loss: 0.2116 - val_accuracy: 0.6725 - val_loss: 1.2169\n",
+ "Epoch 7/10\n",
+ "\u001b[1m450/450\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 188ms/step - accuracy: 0.9484 - loss: 0.1634 - val_accuracy: 0.6400 - val_loss: 1.3700\n",
+ "Epoch 8/10\n",
+ "\u001b[1m450/450\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 170ms/step - accuracy: 0.9553 - loss: 0.1306 - val_accuracy: 0.6625 - val_loss: 1.3624\n",
+ "Epoch 9/10\n",
+ "\u001b[1m450/450\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 157ms/step - accuracy: 0.9769 - loss: 0.0784 - val_accuracy: 0.6550 - val_loss: 1.4636\n",
+ "Epoch 10/10\n",
+ "\u001b[1m450/450\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 163ms/step - accuracy: 0.9779 - loss: 0.0764 - val_accuracy: 0.6500 - val_loss: 1.6482\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Unfreeze the last few layers of the base model for fine-tuning\n",
+ "for layer in base_model.layers[-4:]:\n",
+ " layer.trainable = True\n",
+ "\n",
+ "# Recompile and fine-tune the model\n",
+ "combined_model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
+ "\n",
+ "# Continue training\n",
+ "history_vgg16 = combined_model.fit(X_train[:8000], y_train[:8000], epochs=10, batch_size=16, validation_split=0.1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "313/313 - 38s - 122ms/step - accuracy: 0.6526 - loss: 1.7165\n",
+ "Test loss: 1.7165441513061523\n",
+ "Test accuracy: 0.6525999903678894\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Evaluate on the test data\n",
+ "score = combined_model.evaluate(X_test, y_test, verbose=2)\n",
+ "print(f\"Test loss: {score[0]}\")\n",
+ "print(f\"Test accuracy: {score[1]}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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YyLvvvkvlypVxcHDAzs4OBwcHjhw5kuL9+ttvv1G+fHlatGiR7vHd3d3p168fwcHBlm/R1qxZw/79+1N88yYi+ZfaYnemtlj61Ba7u/zeFrubNWvWULlyZerUqZNifd++fTEMgzVr1gDmn4+rV6/yxBNP8PPPP3Px4sVUx6pTpw6//fYbb7zxBmvXriUmJua+80nWU0FIUujevTuenp7MnDkTgOXLl3Pu3LkUV5oA8y9hSP2L/sknn2Tbtm1s27aNhx9+ONU+Fy5cSHWViREjRlj2SW88+914eXlhMpnw8PBINflgmzZtiI2NzZJumGnlW7RoET169KB48eJ89913/P3332zbto3+/fsTGxubavtChQqlWufo6Jjil+T8+fPp06cP3377LfXr16dgwYL07t2b8PDwO+a7eRna2rVrp2jY2dvbM3/+/FS/rF1cXNLsInsvLl26RGJiIp9//nmqc9/smn7z/Jl9zu70vrj9+bw5sWRGPnTutu/NLq23fsDflNa6u2ncuDHlypXjq6++Ys6cOZahAbe7cuUKhmGk+bh9fX1TZLt06RLFihVLtd3t6zL73ribdevWpTrOnYZqJScnExwcjK+vLzVr1uTq1atcvXqVFi1a4OrqmmLY2IULF/D19b3jnBUXLlzA1tY2zcd+P9J6zocPH85bb71F586d+eWXX9iyZQvbtm0jMDAwxfvswoULlChR4q7nGDJkCNeuXeP7778HYOrUqZQoUYJOnTpl3QMRkVxLbbE7U1ssfWqL3V1+botlxKVLlzL0mJ955hmCgoI4efIk3bp1w9vbm7p161qGsoJ5eOfrr7/OkiVLaNq0KQULFqRz586phs2LdekqY5KCs7MzTzzxBN988w1hYWEEBQXh7u7O448/nmK7li1b8vXXX7N06VJeffVVy3pvb2/LVRbc3d2Ji4tLsc+qVatYvnw53bt3t6wvWbIkJUuWBMDBweGec5crVy7ND2njxpj1jE6IeCdpfWB89913lC5dmvnz56e4/9bHnlmFCxdmypQpTJkyhdDQUJYuXcobb7zB+fPnWbFixR33A1i4cCF+fn53PU9WXj3Jy8vL8q1Jer02SpcuDWT+ObPWVZ5uNlJufoDf6m4NwvT069ePN998E5PJRJ8+fdLcxsvLCxsbG8LCwlLdd3OSxpuvdaFChdLMcvu6zL437qZmzZps27YtxbqbjYW0/P7775Y/WtJqiG/evJn9+/dTuXJlihQpwl9//UVycnK6P7dFihQhKSmJ8PDwOzZSHR0d03xfpTd+Pb2f8d69e/Puu++mWH/x4kUKFCiQItPp06fTzXJT2bJladOmDdOmTaNNmzYsXbqU8ePHp/iGVETyL7XF7kxtsfSpLZYx+bUtlhGFChXK0GMG8/PYr18/oqKiWL9+PW+//Tbt27fn8OHD+Pn54erqyvjx4xk/fjznzp2z9Bbq0KEDBw8evK+cknXUQ0hSGTBgAElJSXz44YcsX76cXr16pbr8ZZcuXahcuTLvvvtuhn+gBw4cSNGiRXnttdfS/EVzv7p160ZkZGSKqxCB+Zs1Nzc3qlSpkuXnBPMHpIODQ4oPyvDw8DSv0nAvSpUqxeDBg2nZsiX//PPPHbdt1aoVdnZ2HDt2jFq1aqW5ZBcXFxeaNm3Kv//+S0BAQJrnvvmhnt3PWVapUKECxYoV48cff0yxPjQ0NNX7LKP69OlDhw4dGDlyJMWLF09zG1dXV+rWrcuiRYtSfLuWnJzMd999R4kSJShfvjxg7vb+xx9/pGgoJSUlpRiCBVn/3nB3d0+1/53+iJgxYwY2NjYsWbKEP//8M8Vy8xLGNydRvflNcnBwcLrHu3mVnenTp98xp7+/P7t3706xbs2aNZkatmAymVJd0njZsmWcOXMmVabDhw9bulPfycsvv8zu3bstE8Q+++yzGc4jInmf2mKZo7aYmdpiGZNf22IZ0bx5c/bv35/qfT579mxMJlOqKxCC+blq06YNY8aMIT4+nn379qXapmjRovTt25cnnniCQ4cOpeqlKNajHkKSSq1atQgICGDKlCkYhpGqizKYx8UuWbKEVq1aUadOHZ599lkeffRRvLy8uHr1Klu2bGHXrl0pLoNaoEABlixZQocOHQgMDOTFF1+kXr16uLm5cenSJdavX094eDgNGjRIca5169Zx4cIFwPzL9eTJkyxcuBCAJk2aUKRIEQBeffVVvv/+ex5//HHeeecdSpQowcKFC1m6dCkfffQRzs7O2fJ8tW/fnkWLFjFo0CC6d+/OqVOneOedd/Dx8bmnLpERERE0bdqUJ598kooVK+Lu7s62bdtYsWIFXbt2veO+/v7+TJgwgTFjxnD8+HFat26Nl5cX586dY+vWrZZK/f1Ys2ZNmt1R27Zty6effsojjzxCo0aNePHFF/H39+fatWscPXqUX375xfKHclY/Z9nFxsaG8ePH8/zzz9O9e3f69+/P1atXGT9+PD4+Pvf0Taevry9Lliy563aTJk2iZcuWNG3alFdffRUHBwe++OIL9u7dy7x58ywNuDfffJOlS5fSrFkzxo4di4uLC9OmTUtxlQd4MO+N9Fy6dImff/6ZVq1apTss6pNPPmH27NlMmjSJJ554gpkzZ/LCCy9w6NAhmjZtSnJyMlu2bKFSpUr06tWLRo0a8cwzzzBx4kTOnTtH+/btcXR05N9//8XFxYUhQ4YA5i7Nb731FmPHjqVJkybs37+fqVOn4unpmeH87du3Jzg4mIoVKxIQEMCOHTv48MMPUw0PGzZsGPPnz6dTp0688cYb1KlTh5iYGNatW0f79u1TNKJatmxJ5cqV+fPPPy2XdhURuUltscxRW8xMbbGMyY9tsVvt2bPH8vN7q9q1a/PKK68we/Zs2rVrx4QJE/Dz82PZsmV88cUXvPjii5Yi2LPPPouzszMNGzbEx8eH8PBwJk2ahKenJ7Vr1wagbt26tG/fnoCAALy8vDhw4ABz5syhfv36qQrcYkVWm85acrRPP/3UAIzKlSvfcbuIiAjj3XffNWrXrm14eHgYdnZ2hre3t9GyZUtj2rRpRlRUVKp9wsPDjVGjRhkBAQGGq6urYW9vb/j6+hodOnQwZs+ebSQkJKTYvkmTJgaQ5vLnn3+m2DY0NNTo1auX4eXlZTg4OBgBAQEZvnLDrUjnyhbpzcr/3nvvGf7+/oajo6NRqVIl45tvvknz6gC3H/emW694FBsba7zwwgtGQECA4eHhYTg7OxsVKlQw3n777RTPZ1pXtrhpyZIlRtOmTQ0PDw/D0dHR8PPzM7p37278/vvvlm369OljuLq6Zvg5uXklifSWm1dQCAkJMfr3728UL17csLe3N4oUKWI0aNDAmDhxYpY+Z+ldaePPP/9M9d5I78oWab2epHFVqq+//tooW7as4eDgYJQvX94ICgoyOnXqlOoqHGm59coW6UnryhaGYRgbNmwwmjVrZri6uhrOzs5GvXr1jF9++SXV/hs3bjTq1atnODo6GsWKFTNGjhxpfP311ylel5sy8t7I6itbTJkyxQCMJUuWpLvNl19+aQDGTz/9ZBiGYcTExBhjx441ypUrZzg4OBiFChUymjVrZmzatMmyT1JSkvHJJ58YVatWNRwcHAxPT0+jfv36KZ6juLg447XXXjNKlixpODs7G02aNDF27tyZ7lXG0rpyy5UrV4wBAwYY3t7ehouLi/HII48YGzZsSPW+urntyy+/bJQqVcqwt7c3vL29jXbt2hkHDx5Mddxx48YZgLF58+aMPpUiko+oLaa22O3UFlNb7H7cfM7TW24+9pMnTxpPPvmkUahQIcPe3t6oUKGC8eGHH6a4+uCsWbOMpk2bGkWLFjUcHBwMX19fo0ePHsbu3bst27zxxhtGrVq1DC8vL8PR0dEoU6aM8corrxgXL17M0scl98dkGDcG9YqISIZcvXqV8uXL07lzZ77++mtrx5FcqlatWphMplTj/0VEROTO1BYTyRoaMiYicgfh4eH873//o2nTphQqVIiTJ0/yySefcO3aNV5++WVrx5NcJjIykr179/Lrr7+yY8cOFi9ebO1IIiIiOZraYiLZRwUhEZE7cHR05MSJEwwaNIjLly/j4uJCvXr1+PLLL7NtckzJu/755x9Lg/btt9+mc+fO1o4kIiKSo6ktJpJ9NGRMRERERERERCSf0WXnRURERERERETyGRWERERERERERETyGRWERERERERERETymXw3qXRycjJnz57F3d0dk8lk7TgiIiJyB4ZhcO3aNXx9fbGx0fdY1qL2k4iISO6QmbZTvisInT17lpIlS1o7hoiIiGTCqVOnKFGihLVj5FtqP4mIiOQuGWk75buCkLu7O2B+cjw8PKycRkRERO4kMjKSkiVLWj6/xTrUfhIREckdMtN2yncFoZvdnD08PNSgERERySU0TMm61H4SERHJXTLSdtJgfBERERERERGRfEYFIRERERERERGRfEYFIRERERERERGRfCbfzSGUEYZhkJiYSFJSkrWjiGQ5W1tb7OzsNB+HiIhkKbWfJLuo7SIikj1UELpNfHw8YWFhREdHWzuKSLZxcXHBx8cHBwcHa0cREZE8QO0nyW5qu4iIZD0VhG6RnJxMSEgItra2+Pr64uDgoG8iJE8xDIP4+HguXLhASEgI5cqVw8ZGI0dFROTeqf0k2UltFxGR7KOC0C3i4+NJTk6mZMmSuLi4WDuOSLZwdnbG3t6ekydPEh8fj5OTk7UjiYhILqb2k2Q3tV1ERLKHyutp0LcOktfpPS4iIllNny2SnfT+EhHJevrNKiIiIiIiIiKSz6ggJCIiIiIiIiKSz6ggJOl69NFHGTZsmLVjiIiIiOQaaj+JiEhuoYJQHmAyme649O3b956Ou2jRIt55550sybhp0yZsbW1p3bp1lhxPRERE5H7k5PZT37596dy5830dQ0RE5G50lbE8ICwszPL/+fPnM3bsWA4dOmRZ5+zsnGL7hIQE7O3t73rcggULZlnGoKAghgwZwrfffktoaCilSpXKsmNnVkYfv4iIiORduaH9JCIieVhSIiTGgKO71SKoIHQXhmEQk5BklXM729tiMpnuul2xYsUs//f09MRkMlnWnThxAh8fH+bPn88XX3zB5s2bmT59Oh07dmTw4MFs2LCBy5cv89BDDzF69GieeOIJy7EeffRRqlevzpQpUwDw9/fnueee4+jRoyxYsAAvLy/efPNNnnvuuTvmi4qK4scff2Tbtm2Eh4cTHBzM2LFjU2yzdOlSJkyYwN69e3Fzc6Nx48YsWrQIgLi4ON566y3mzZvH+fPnKVWqFG+88QYDBgwgODiYYcOGcfXqVcuxlixZQpcuXTAMA4Bx48axZMkShg4dysSJEzlx4gRJSUmsXLmSiRMnsnfvXmxtbalfvz6ffvopDz30kOVYp0+f5tVXX2XVqlXExcVRqVIlpk2bRtGiRSlTpgxbt26lVq1alu0///xzPvroI06cOJGh105E8qekZIMtIZf4dXcYO0OvYjKBnY0JO1sbbG1M2NmYbvnXBnvblLfN295y+8b99rfdthzH1ua2Y5qwT3Uum1uOab5te8t57GxssL3lfvvbbtvZmPR7Tyys1X7KaNsJcn776U7WrVvHyJEj2bVrFwULFqRPnz5MnDgROztz037hwoWMHz+eo0eP4uLiQo0aNfj5559xdXVl7dq1vPbaa+zbtw97e3uqVKnC3Llz8fPzu+c8IiKSSTFXYWE/MAx4aiHYWqc0o4LQXcQkJFF57EqrnHv/hFa4OGTNS/T6668zefJkZs6ciaOjI7GxsdSsWZPXX38dDw8Pli1bxjPPPEOZMmWoW7duuseZPHky77zzDqNHj2bhwoW8+OKLNG7cmIoVK6a7z/z586lQoQIVKlTg6aefZsiQIbz11luWBtuyZcvo2rUrY8aMYc6cOcTHx7Ns2TLL/r179+bvv//ms88+IzAwkJCQEC5evJipx3/06FF+/PFHfvrpJ2xtbQFzoWr48OFUq1aNqKgoxo4dS5cuXdi5cyc2NjZcv36dJk2aULx4cZYuXUqxYsX4559/SE5Oxt/fnxYtWjBz5swUBaGZM2fSt29f/VEkIqkkJxvsCL3Cr7vOsnxvOBeuxVk7UpZbPKgBNUp5WTuG5ADWaj9lZdsJrNt+Ss+ZM2do27Ytffv2Zfbs2Rw8eJBnn30WJycnxo0bR1hYGE888QQffPABXbp04dq1a2zYsAHDMEhMTKRz5848++yzzJs3j/j4eLZu3ap2i4jIg3TpGMztCZeOgL0LnNsLvtWtEkUFoXxi2LBhdO3aNcW6V1991fL/IUOGsGLFChYsWHDHBk3btm0ZNGgQYG4kffLJJ6xdu/aODZoZM2bw9NNPA9C6dWuuX7/OH3/8QYsWLQD43//+R69evRg/frxln8DAQAAOHz7Mjz/+yOrVqy3blylTJjMPHYD4+HjmzJlDkSJFLOu6deuWKqe3tzf79++natWqzJ07lwsXLrBt2zZL9++yZctath84cCAvvPACH3/8MY6OjuzatYudO3daejaJiBiGwc5TV/l1dxjL94QRFhFruc/T2Z7WVYrRrJI3jnY2JCUbJCYbln8Tk5JT3E66cduyLskgKfm/dbfeTko2SLjt9n/rk9M8V8p1ySQm3Xqu/+6/uS4tdjaamlDyFmu2n9LzxRdfULJkSaZOnYrJZKJixYqcPXuW119/nbFjxxIWFkZiYiJdu3a19PqpVq0aAJcvXyYiIoL27dtbekRXqlQp0xlEROQeHV8HP/aG2KvgURyemAc+gVaLo4LQXTjb27J/QiurnTur3NqLBSApKYn33nuP+fPnc+bMGeLi4oiLi8PV1fWOxwkICLD8/2bX6vPnz6e7/aFDh9i6daulSGJnZ0fPnj0JCgqyFHh27tzJs88+m+b+O3fuxNbWliZNmmTocabHz88vRTEI4NixY7z11lts3ryZixcvkpycDEBoaChVq1Zl586d1KhRI925ADp37szgwYNZvHgxvXr1IigoiKZNm+Lv739fWUUkdzMMg31nI/ll91mW7Q7j9JUYy31ujnY8VqUoHQJ8aVi2MA52ubOAYhgpi0zm4lEyHs6an03MrNV+ysq2E1iv/XQnBw4coH79+il69TRs2JDr169z+vRpAgMDad68OdWqVaNVq1Y89thjdO/eHS8vLwoWLEjfvn1p1aoVLVu2pEWLFvTo0QMfH597yiIiIpmwPQiWj4TkRCheC3p9D+7F7r5fNlJB6C5MJlOWdj22ltsbKpMnT+aTTz5hypQpVKtWDVdXV4YNG0Z8fPwdj3P7ZIomk8lSSEnLjBkzSExMpHjx4pZ1hmFgb2/PlStX8PLySjVp463udB+AjY2NZa6gmxISElJtl1ZDrUOHDpQsWZJvvvkGX19fkpOTqVq1quU5uNu5HRwceOaZZ5g5cyZdu3Zl7ty5lvkCRCT/ORR+jV92nWXZnjBCLkZZ1rs42NK8UlHaB/jQpHwRnLL4D1ZrMJluzC2U+x+KZBO1n1LKbPvpTgzDSDXE62ZbyGQyYWtry+rVq9m0aROrVq3i888/Z8yYMWzZsoXSpUszc+ZMhg4dyooVK5g/fz5vvvkmq1evpl69eveUR0RE7iIpEVaOhq1fmW9X7Q6dpoL9nf/efBBy/ye13JMNGzbQqVMny1Cu5ORkjhw5kqXdhhMTE5k9ezaTJ0/mscceS3Fft27d+P777xk8eDABAQH88ccf9OvXL9UxqlWrRnJyMuvWrbP0KLpVkSJFuHbtGlFRUZZG286dO++a7dKlSxw4cICvvvqKRo0aAfDXX3+l2CYgIIBvv/2Wy5cvp9tLaODAgVStWpUvvviChISEVN3KRSRvO3bhOr/uCuPX3Wc5cv66Zb2jnQ3NKnrTPsCXZhW9cXZQ5UQkL3gQ7ae7qVy5Mj/99FOKwtCmTZtwd3e3fAFnMplo2LAhDRs2ZOzYsfj5+bF48WKGDx8OQI0aNahRowajRo2ifv36zJ07VwUhEZHscHPy6GNrzLebvQmNXoUcMnebCkL5VNmyZfnpp5/YtGkTXl5efPzxx4SHh2dpg+bXX3/lypUrDBgwAE9PzxT3de/enRkzZjB48GDefvttmjdvzkMPPUSvXr1ITEzkt99+47XXXsPf358+ffrQv39/y6TSJ0+e5Pz58/To0YO6devi4uLC6NGjGTJkCFu3biU4OPiu2by8vChUqBBff/01Pj4+hIaG8sYbb6TY5oknnuDdd9+lc+fOTJo0CR8fH/799198fX2pX78+YB53X69ePV5//XX69+9/115FIpL7hV6K5pfdZ/l1dxgHwiIt6x1sbWhcvggdAn1oXqkobo76iBXJax5E++mmiIiIVF9yFSxYkEGDBjFlyhSGDBnC4MGDOXToEG+//TbDhw/HxsaGLVu28Mcff/DYY4/h7e3Nli1buHDhApUqVSIkJISvv/6ajh074uvry6FDhzh8+DC9e/fO8vwiIvne7ZNHd/kKKne0dqoU1FrNp9566y1CQkJo1aoVLi4uPPfcc3Tu3JmIiIgsO8eMGTNo0aJFqmIQmHsIvfvuu/zzzz88+uijLFiwgHfeeYf33nsPDw8PGjdubNl2+vTpjB49mkGDBnHp0iVKlSrF6NGjAXPD6LvvvmPkyJF8/fXXtGjRgnHjxt31Uq42Njb88MMPDB06lKpVq1KhQgU+++wzHn30Ucs2Dg4OrFq1ihEjRtC2bVsSExOpXLky06ZNS3GsAQMGsGnTJvr3738fz5aI5GRnrsaw7EYRaPfp/35P2tmYeKRcYdoH+NKyclE8NYeOSJ72INpPN61du5YaNWqkWNenTx+Cg4NZvnw5I0eOJDAwkIIFCzJgwADefPNNADw8PFi/fj1TpkwhMjISPz8/Jk+eTJs2bTh37hwHDx5k1qxZXLp0CR8fHwYPHszzzz+f5flFRPK1HDZ5dHpMxu0TsORxkZGReHp6EhERgYeHR4r7YmNjCQkJoXTp0jg5OVkpoeQ2//vf//jhhx/Ys2ePtaNkmN7rInd3LjKWZbvNw8H+Cb1qWW9jgvoPFaJ9gC+tqxTDy9XBeiHzgTt9budX69ev58MPP2THjh2EhYWxePFiOnfufMd94uLimDBhAt999x3h4eGUKFGCMWPGZPjLDLWfxNr0PhORXMPKk0dnpu2kHkIi9+j69escOHCAzz//nHfeecfacUQkC1y8Hsdve8P5dddZtp64zM2vTEwmqO1fkA4BPrSu6kMRd0frBpV8LSoqisDAQPr160e3bt0ytE+PHj04d+4cM2bMoGzZspw/f57ExMRsTioiIpKP3D55dLXHoePnOWLy6PSoICRyjwYPHsy8efPo3LmzhouJ5GJXo+NZsTecX3eHsenYRZJv6Tf7cKkCtA/wpW01H4p56htpyRnatGlDmzZtMrz9ihUrWLduHcePH7dcJMHf3z+b0omIiORDqSaPfgsajcgxk0enRwUhkXsUHBycoQmsRSTniYxNYNW+c/y6+yx/HblI4i1VoIASnrQP8KFtNR9KeLlYMaVI1li6dCm1atXigw8+YM6cObi6utKxY0feeeeddC+GEBcXR1xcnOV2ZGRkmtuJiIjke7lg8uj0qCAkIiL5QlRcIr8fOMcvu8JYf/gC8UnJlvsqFnOnQ6Av7QN88CvkasWUIlnv+PHj/PXXXzg5ObF48WIuXrzIoEGDuHz5MkFBQWnuM2nSJMaPH/+Ak4qIiOQyuWTy6PSoICQiInlWTHwSaw6e59fdZ1lz8Dxxif8Vgcp6u9E+wIf2Ab6U9XazYkqR7JWcnIzJZOL777+3XPnz448/pnv37kybNi3NXkKjRo1i+PDhltuRkZGULFnygWUWERHJ8bbNgN9es9rk0VlBBSEREclT4hKTWHfoAr/uDuP3A+eIjk+y3OdfyIX2Ab60D/ShQlF3TDl8XLdIVvDx8aF48eKWYhBApUqVMAyD06dPU65cuVT7ODo64uioydNFRERSSUqElaNg69fm29Ueh45TwT73zTepgpCIiOR68YnJbDx6kV92n2X1vnNci/vv6knFCzjTPtCHDgG+VPH1UBFI8p2GDRuyYMECrl+/jpubuTfc4cOHsbGxoUSJElZOJyIikovk0smj06OCkIiI5EqJScn8ffwSv+4KY8W+cCJiEiz3FfNwol2AD+0DfKhesoCKQJKnXL9+naNHj1puh4SEsHPnTgoWLEipUqUYNWoUZ86cYfbs2QA8+eSTvPPOO/Tr14/x48dz8eJFRo4cSf/+/dOdVFpERERuk4snj06PCkIiIpKjJSUbnL0aw7EL1zl+IYrjF68TcjGK/WcjuRL9XxGosJsjbasVo32AL7X8vLCxURFI8qbt27fTtGlTy+2bc/306dOH4OBgwsLCCA0Ntdzv5ubG6tWrGTJkCLVq1aJQoUL06NGDiRMnPvDsIiIiuVIunzw6PSoIicWjjz5K9erVmTJlCgD+/v4MGzaMYcOGpbuPyWRi8eLFdO7c+b7OnVXHEZHc62p0PMcuRHH8grngc7P4c+JSNPG3TAZ9Ky8Xe1pX9aFDgA91yxTCVkUgyQceffRRDMNI9/7g4OBU6ypWrMjq1auzMVX+pfaTiEget20GLB8JRtKNyaPngntRa6fKEioI5QEdOnQgJiaG33//PdV9f//9Nw0aNGDHjh08/PDDmTrutm3bcHXN2ssvjxs3jiVLlrBz584U68PCwvDy8srSc6UnJiYGX19fTCYTZ86cUXd5kQcoLjGJU5ejbxR+zMWf4xejCLkYxeWo+HT3c7C1wb+wC6ULu1KmiBtlCrvykLcb1Yp7Ym9r8wAfgYjkFWo/ZUxwcDDDhg3j6tWr2XoeEZEcJw9NHp0eFYTygAEDBtC1a1dOnjyJn59fivuCgoKoXr16phszAEWKFMmqiHdVrNiDuzzfTz/9RNWqVTEMg0WLFvHUU089sHPfzjAMkpKSsLPTj6LkHYZhcP5anGWIl7m3j7nwc+pyNMnpd2ygmIcTZYq4mpfCbpQu4spDhd0o7uWs3j8ikqXUfhIRkXTFXIEF/eD4n+bbuXzy6PToa9W7MQyIj7LOcofu4Ldq37493t7eqbqIR0dHM3/+fAYMGMClS5d44oknKFGiBC4uLlSrVo158+bd8bj+/v6W7s8AR44coXHjxjg5OVG5cuU0u56//vrrlC9fHhcXF8qUKcNbb71FQoJ5jo/g4GDGjx/Prl27MJlMmEwmS2aTycSSJUssx9mzZw/NmjXD2dmZQoUK8dxzz3H9+nXL/X379qVz58589NFH+Pj4UKhQIV566SXLue5kxowZPP300zz99NPMmDEj1f379u2jXbt2eHh44O7uTqNGjTh27Jjl/qCgIKpUqYKjoyM+Pj4MHjwYgBMnTmAymVJ8e3f16lVMJhNr164FYO3atZhMJlauXEmtWrVwdHRkw4YNHDt2jE6dOlG0aFHc3NyoXbt2qm8s4+LieO211yhZsiSOjo6UK1eOGTNmYBgGZcuW5aOPPkqx/d69e7GxsUmRXSQrRcUlsvdMBEt3nWXK74cZOu9f2n++gapvr6Tuu3/w5DdbeHPJXmb8FcKfhy5w8pK5GOTqYEu14p50qu7LsBbl+OyJGvw65BH2jW/F5tHNmftsPSZ2rkb/R0rTtII3pQq5qBgkkttYq/2UwbYTqP2U2fZTekJDQ+nUqRNubm54eHjQo0cPzp07Z7l/165dNG3aFHd3dzw8PKhZsybbt28H4OTJk3To0AEvLy9cXV2pUqUKy5cvv+csIiJZ4tIx+LaFuRhk7wI9v4PGr+a5YhCoh9DdJUTDu77WOffos+Bw9y7HdnZ29O7dm+DgYMaOHWu5ms6CBQuIj4/nqaeeIjo6mpo1a/L666/j4eHBsmXLeOaZZyhTpgx169a96zmSk5Pp2rUrhQsXZvPmzURGRqY5Nt7d3Z3g4GB8fX3Zs2cPzz77LO7u7rz22mv07NmTvXv3smLFCkuxw9PTM9UxoqOjad26NfXq1WPbtm2cP3+egQMHMnjw4BSNtj///BMfHx/+/PNPjh49Ss+ePalevTrPPvtsuo/j2LFj/P333yxatAjDMBg2bBjHjx+nTJkyAJw5c4bGjRvz6KOPsmbNGjw8PNi4cSOJieZLWE+fPp3hw4fz3nvv0aZNGyIiIti4ceNdn7/bvfbaa3z00UeUKVOGAgUKcPr0adq2bcvEiRNxcnJi1qxZdOjQgUOHDlGqVCkAevfuzd9//81nn31GYGAgISEhXLx4EZPJRP/+/Zk5cyavvvqq5RxBQUE0atSIhx56KNP5RG5KSjY4cyWGYxevW4Z43ZzfJzwyNt39bG1MlPRypkwRtxvDvMw9fh4q4koRd0dd9Uskr7NW+ymDbSdQ+ykz7af0GIZB586dcXV1Zd26dSQmJjJo0CB69uxp+TLsqaeeokaNGkyfPh1bW1t27tyJvb09AC+99BLx8fGsX78eV1dX9u/fj5ubW6ZziIhkmTw6eXR6VBDKI/r378+HH37I2rVrLVceCQoKomvXrnh5eeHl5ZWiWDBkyBBWrFjBggULMtSg+f333zlw4AAnTpygRIkSALz77ru0adMmxXZvvvmm5f/+/v6MGDGC+fPn89prr+Hs7Iybmxt2dnZ37OL8/fffExMTw+zZsy1j8KdOnUqHDh14//33KVrUPIGXl5cXU6dOxdbWlooVK9KuXTv++OOPOzZogoKCaNOmjWW8fevWrQkKCrJcaWXatGl4enryww8/WBor5cuXt+w/ceJERowYwcsvv2xZV7t27bs+f7ebMGECLVu2tNwuVKgQgYH//aKZOHEiixcvZunSpQwePJjDhw/z448/snr1alq0aAFgKWIB9OvXj7Fjx7J161bq1KlDQkIC3333HR9++GGms0n+dCUqnuM3iz43h3hdiOLkpWjik9Ke0BmgoKsDZW4WfG4Ufx4q4kqpgq442KkTqojkbGo/Zaz9dKfHt3v3bkJCQihZsiQAc+bMoUqVKmzbto3atWsTGhrKyJEjqVixIgDlypWz7B8aGkq3bt2oVq0akLJtIyLywOXhyaPTo4LQ3di7mL9tsta5M6hixYo0aNCAoKAgmjZtyrFjx9iwYQOrVq0CICkpiffee4/58+dz5swZ4uLiiIuLy/CkhwcOHKBUqVKWxgxA/fr1U223cOFCpkyZwtGjR7l+/TqJiYl4eHhk+HHcPFdgYGCKbA0bNiQ5OZlDhw5ZGjRVqlTB1tbWso2Pjw979uxJ97hJSUnMmjWLTz/91LLu6aef5pVXXmH8+PGWb60aNWpkKQbd6vz585w9e5bmzZtn6vGkpVatWiluR0VFMX78eH799VfOnj1LYmIiMTExlssG79y5E1tbW5o0aZLm8Xx8fGjXrh1BQUHUqVOHX3/9ldjYWB5//PH7zip5R2RsAqcuR3PqcvSNos9/8/vcevn22znY2VC6kLnoY5nUuYgrZQq7UsDF4QE+AhHJNazVfspE2wnUfoK7t5/uds6SJUtaikEAlStXpkCBAhw4cIDatWszfPhwBg4cyJw5c2jRogWPP/64pffy0KFDefHFF1m1ahUtWrSgW7duBAQE3FMWEZF7lmry6B7Q8fM8NXl0elQQuhuTKcNdj61twIABDB48mGnTpjFz5kz8/PwsxYvJkyfzySefMGXKFKpVq4arqyvDhg0jPj79q/rcKq3L294+5GPz5s306tWL8ePH06pVK0tPm8mTJ2fqcRiGke5wklvX3160MZlMJCen35Nh5cqVnDlzhp49e6ZYn5SUxKpVq2jTps0drzh2t6uR2djYWPLflN6Y/NsbkiNHjmTlypV89NFHlC1bFmdnZ7p37255fTJyJbSBAwfyzDPP8MknnzBz5kx69uyJi0vmGsaSu8UnJnPmagynLkcTejmaU1eiOX05xvL/q3co+gD4ejpR+sbQrps9fsoUdsW3gCZ0FpFMUvsJyBvtp3s5563rx40bx5NPPsmyZcv47bffePvtt/nhhx/o0qULAwcOpFWrVixbtoxVq1YxadIkJk+ezJAhQ+4pj4hIpuWTyaPTo4JQHtKjRw9efvll5s6dy6xZs3j22WctH8YbNmygU6dOPP3004B5TPuRI0eoVKlSho5duXJlQkNDOXv2LL6+5jkB/v777xTbbNy4ET8/P8aMGWNZd/LkyRTbODg4kJSUdNdzzZo1i6ioKEvhZOPGjdjY2KQYvpVZM2bMoFevXinyAbz33nvMmDGDNm3aEBAQwKxZs0hISEjVYHJ3d8ff358//vjD0q38VjevKhIWFkaNGjUAUl0eNj0bNmygb9++dOnSBYDr169z4sQJy/3VqlUjOTmZdevWWYaM3a5t27a4uroyffp0fvvtN9avX5+hc0vuYRgGF67FWQo8oZdiOHUl2tLrJywy9q7zqRZydaBEQRdKF3JJMb9P6cKuuDjoI0FE8h+1n+7dzcd36tQpSy+h/fv3ExERkeI5Kl++POXLl+eVV17hiSeeYObMmZY2T8mSJXnhhRd44YUXGDVqFN98840KQiLyYFw6BnN7wKWj5h6mXb+GSh2sneqBUus/D3Fzc6Nnz56MHj2aiIgI+vbta7mvbNmy/PTTT2zatAkvLy8+/vhjwsPDM9ygadGiBRUqVKB3795MnjyZyMjIVIWVsmXLEhoayg8//EDt2rVZtmwZixcvTrGNv78/ISEh7Ny5kxIlSuDu7o6jo2OKbZ566inefvtt+vTpw7hx47hw4QJDhgzhmWeesXR3zqwLFy7wyy+/sHTpUqpWrZrivj59+tCuXTsuXLjA4MGD+fzzz+nVqxejRo3C09OTzZs3U6dOHSpUqMC4ceN44YUX8Pb2pk2bNly7do2NGzcyZMgQnJ2dqVevHu+99x7+/v5cvHgxxZwAd1K2bFkWLVpEhw4dMJlMvPXWWym+rfP396dPnz7079/fMqn0yZMnOX/+PD169ADA1taWvn37MmrUKMqWLZtml3TJ+a7FJpgLPpdjOH3lRk+fGz1+Tl+JIS7xzt/iOtvbUrKgMyW9XChZ0LyUKuhiWefqqF/7IiK3Uvvp7pKSklJ9yeXg4ECLFi0ICAjgqaeeYsqUKZZJpZs0aUKtWrWIiYlh5MiRdO/endKlS3P69Gm2bdtGt27dABg2bBht2rShfPnyXLlyhTVr1mT4uRURuS/5bPLo9OgvgzxmwIABzJgxg8cee8xydSqAt956i5CQEFq1aoWLiwvPPfccnTt3JiIiIkPHtbGxYfHixQwYMIA6derg7+/PZ599RuvWrS3bdOrUiVdeeYXBgwcTFxdHu3bteOuttxg3bpxlm27durFo0SKaNm3K1atXmTlzZoqGF4CLiwsrV67k5Zdfpnbt2ri4uNCtWzc+/vjje35ebk6wmNb8PzcvhTpnzhyGDx/OmjVrGDlyJE2aNMHW1pbq1avTsGFDwFw8io2N5ZNPPuHVV1+lcOHCdO/e3XKsoKAg+vfvT61atahQoQIffPABjz322F3zffLJJ/Tv358GDRpQuHBhXn/9dSIjI1NsM336dEaPHs2gQYO4dOkSpUqVYvTo0Sm2GTBgAO+++y79+/e/l6dJHoD4xGTOXv1vGFfoZfOwrpv/v9uwLhsT+Ba4WfBxvlHsubF4uVDYzUFX8BIRySS1n+7s+vXrlt7PN/n5+XHixAmWLFnCkCFDaNy4MTY2NrRu3ZrPP/8cMH9ZdenSJXr37s25c+coXLgwXbt2Zfz48YC50PTSSy9x+vRpPDw8aN26NZ988sl95xURuaNt38Ly1/LV5NHpMRlpDW7OwyIjI/H09CQiIiLVZH2xsbGEhIRQunRpnJzy/gRSkvds3LiRRx99lNOnT9/x20C917PPzWFdpyy9e2IsvXxOX4khLCKG5AwO6yrp9V/Bp9SNgo9PASfsbXX1Lsk/7vS5LQ+O2k9ibXqfich9S0qEFW/Atm/Mt/Po5NGZaTuph5BIHhAXF8epU6d466236NGjx313DZc7uxabYCn0nL4SfcskzuYJne82rMvJ3sZS4Pmvd48zpQq5UMLLBTcN6xIRERERyTr5fPLo9OivDpE8YN68eQwYMIDq1aszZ84ca8fJk0IuRvHOr/v5N/TKHS/RDhrWJSIiIiKSY2jy6HSpICSSB/Tt2zfVXAKSNZKTDWb9fYL3VxwkNuG/nj8FXR3+69lzS7GnVEEN6xIRERERyRGOr4Uf+9yYPLrEjcmjA6ydKsdQQUhEJB2nLkfz6oJdbAm5DMAjZQvzeuuKlC7iqmFdIiIiIiI5mSaPviv9RZOGfDbPtuRDeo/fmWEYfL8llHeXHyA6PgkXB1tGta3E03VLaaiXiEg69Nki2UnvLxHJsHwyeXRWUEHoFvb29gBER0fj7Oxs5TQi2Sc6Ohr47z0v/zl7NYbXf9rNhiMXAahTuiAfdQ+kVCEXKycTEcmZ1H6SB0FtFxHJkNsnj24+Fh4Znu8nj06PCkK3sLW1pUCBApw/fx4AFxcX9QaQPMUwDKKjozl//jwFChTA1tbW2pFyDMMwWLDjNO/8sp9rcYk42tnweuuK9G3gj42Nfg+IiKRH7SfJTmq7iEiGXTwK83pq8uhMUEHoNsWKFQOwNGpE8qICBQpY3usC5yNjGbVoD38cNP/c1yhVgMmPB1KmiJuVk4mI5A5qP0l2U9tFRO7o+Fr4sTfERmjy6ExQQeg2JpMJHx8fvL29SUi486WlRXIje3t7fbt2g2EYLN11lrE/7yMiJgEHWxuGP1aeZxuVwVa9gkREMkztJ8lOaruIyB1p8uh7poJQOmxtbfXBI5KHXbwex5uL97JiXzgA1Yp7MrlHIOWLuls5mYhI7qX2k4iIPDCaPPq+qSAkIvnOb3vCGLNkL5ej4rGzMTGkWTkGNX0Ie1sba0cTEREREZG7ibkCC/qah4qBJo++RyoIiUi+cSUqnreX7mPprrMAVCzmzkePB1K1uKeVk4mIiIiISIZcOAQ/PKnJo7OACkIiki/8vv8coxbv4cK1OGxtTLzY5CGGNi+Hg516BYmIiIiI5HhndsDGz+DAUjCSNXl0FlBBSETytIiYBCb8sp+f/jkNQFlvNyY/HkhgyQLWDSYiIiIiIneWnAxHV5sLQSf/+m99uVbm+YI0efR9UUFIRPKsdYcv8MZPuwmLiMVkgmcblWF4y/I42WvCUxERERGRHCsxDvYsgE2fw4WD5nU2dlDtcWgwBIpWsW6+PEIFIRHJc67HJfK/ZQeYtzUUAP9CLnz0eCC1/AtaOZmIiIiIiKQr5ipsD4ItX8F189WAcXCHWn2h7ovgWdya6fIcFYREJE/ZdOwiry3czekrMQD0beDPa60r4OKgX3ciIiIiIjnS1VOweTr8Mwvir5vXuftCvRegZl9w0kVgsoP+QhKRPCE6PpEPVhwieNMJAEp4OfNB9wAaPFTYusFERERERCRtYbvNw8L2/gRGknmdd2VoMBSqdgM7B+vmy+NUEBKRXG/7icu8umAXJy5FA/BEnVKMaVcJN0f9ihMRERERyVEMA46tgU2fwfG1/60v3RgavAxlm4PJZLV4+Yn+WhKRXCs2IYmPVx/mmw3HMQwo5uHE+90DaFK+iLWjiYiIiIjIrZISYO8ic4+gc3vM60y2UKWLeaJo3+pWjZcfqSAkIrnSrlNXGbFgF0fPm8cYd3u4BGM7VMbT2d7KyURERERExCLuGuyYZZ4jKPK0eZ29KzzcG+q9CF5+1s2Xj6kgJCK5SnxiMp/9cYTp646RlGxQ2M2RSV2r0bJyUWtHExERERGRmyLDYMuXsH0mxEWY17l6Q93noVZ/cNEVgK1NBSERyTX2nY1gxI+7OBh+DYAOgb5M6FgFL1dNNiciIiIikiOcP2AeFrb7R0hOMK8rVM48LCygJ9g7WTefWNhYO4CIyN0kJJl7BXWaupGD4dco6OrAF089zOdP1FAxSETynfXr19OhQwd8fX0xmUwsWbIkw/tu3LgROzs7qlevnm35REQkHzIMCNkA3z8OX9SDnd+bi0GlGsATP8BLW6FmHxWDchj1EBKRHO3wuWuM+HEXe86Yu5m2qlKU/3WpRmE3RysnExGxjqioKAIDA+nXrx/dunXL8H4RERH07t2b5s2bc+7cuWxMKCIi+UZSIhz42dwj6Oy/N1aaoFIHaPgylKhl1XhyZyoIiUiOlJRs8M2G43y86jDxScl4OtszoVMVOgaavxEXEcmv2rRpQ5s2bTK93/PPP8+TTz6Jra1tpnoViYiIpBIfBf9+B39Pg6snzevsnKD6U1D/JSj0kHXzSYZYfcjYF198QenSpXFycqJmzZps2LDhjttPmzaNSpUq4ezsTIUKFZg9e/YDSioiD8rxC9fp/uUm3vvtIPFJyTSr6M2qVxrTqXpxFYNERO7BzJkzOXbsGG+//XaGto+LiyMyMjLFIiIiwvXzsGYifFIFfnvNXAxyKQSPjoJX9kH7j1UMykWs2kNo/vz5DBs2jC+++IKGDRvy1Vdf0aZNG/bv30+pUqVSbT99+nRGjRrFN998Q+3atdm6dSvPPvssXl5edOjQwQqPQESyUnKywcxNJ/hgxUHiEpNxd7TjrQ6VebxmCRWCRETu0ZEjR3jjjTfYsGEDdnYZa/pNmjSJ8ePHZ3MyERHJNS4egb+nws55kBRnXudVGhoMhsAnwcHFuvnknli1IPTxxx8zYMAABg4cCMCUKVNYuXIl06dPZ9KkSam2nzNnDs8//zw9e/YEoEyZMmzevJn3339fBSGRXC70UjSvLtzF1pDLADQqV5j3ugVQvICzlZOJiOReSUlJPPnkk4wfP57y5ctneL9Ro0YxfPhwy+3IyEhKliyZHRFFRCQnC90MGz+DQ8sBw7yueC1oOBQqtgcbW6vGk/tjtYJQfHw8O3bs4I033kix/rHHHmPTpk1p7hMXF4eTU8pZyZ2dndm6dSsJCQnY29unuU9cXJzltro8i+QshmHw3ZZQJi0/QHR8Ei4OtoxuW4mn6pZSryARkft07do1tm/fzr///svgwYMBSE5OxjAM7OzsWLVqFc2aNUu1n6OjI46OmrxfRCRfSk4yF4A2fgant/63vkJb86XjS9UHtdPzBKsVhC5evEhSUhJFixZNsb5o0aKEh4enuU+rVq349ttv6dy5Mw8//DA7duwgKCiIhIQELl68iI+PT6p91OVZJOc6czWG1xfu5q+jFwGoU7ogH3UPpFQhdTkVEckKHh4e7NmzJ8W6L774gjVr1rBw4UJKly5tpWQiIpLjJMTAzrnmiaIvHzOvs3WAwF5QfwgUyXhPU8kdrH6Vsdt7ABiGkW6vgLfeeovw8HDq1auHYRgULVqUvn378sEHH2Brm3ZXNXV5Fsl5DMNgwfbTvPPrfq7FJeJoZ8PrrSvSt4E/Njb6tkFE5E6uX7/O0aNHLbdDQkLYuXMnBQsWpFSpUowaNYozZ84we/ZsbGxsqFq1aor9vb29cXJySrVeRETyqahLsO1b2Po1RJu/qMWpANQeAHWeB/eid9xdci+rFYQKFy6Mra1tqt5A58+fT9Vr6CZnZ2eCgoL46quvOHfuHD4+Pnz99de4u7tTuHDhNPdRl2eRnOVcZCxv/LSbPw9dAKBGqQJMfjyQMkXcrJxMRCR32L59O02bNrXcvvnFV58+fQgODiYsLIzQ0FBrxRMRyXuSEuHMdkhKAHtn8+XV7ZzA3gnsnG/865T75tO5HGLuDfTvd5AYY17nWcp82fgaT4Oj2ud5nckwDMNaJ69bty41a9bkiy++sKyrXLkynTp1SnNS6bQ0adKE4sWLM3fu3AxtHxkZiaenJxEREXh4eNxTbhHJPMMw+HnnWd5euo+ImAQcbG0Y/lh5nm1UBlv1ChKRdOhzO2fQ6yAi+dalY7D4eTi97e7b2tinUTByurHOMWXxyLI+neJSevfbOf633t4ZbOwyP5/PmR3m+YEOLAUj2bzOJxAaDIXKncHW6gOJ5D5k5jPbqq/08OHDeeaZZ6hVqxb169fn66+/JjQ0lBdeeAEgRZdngMOHD7N161bq1q3LlStX+Pjjj9m7dy+zZs2y5sMQkbu4eD2OMYv3sHLfOQCqFfdkco9Ayhd1t3IyEREREZE0GAZsnwGr3oKEaHBwAw9fSIyFhNgb/8ZAcsJ/+yQnQFwCxD3ACxmZbG4pJKVRMLJzTFlcunQMQm+5iFPZluaJoks31kTR+ZBVC0I9e/bk0qVLTJgwgbCwMKpWrcry5cvx8/MDSNXlOSkpicmTJ3Po0CHs7e1p2rQpmzZtwt/f30qPQETuZuepqzw7ezsXrsVhZ2NiaPNyvPjoQ9jb2lg7moiIiIhIapFhsHQwHP3dfLt0Y+g8HTxLpN42OSllkehmocjy/1jzcKy73Z8Y99/6hBu3Lfvddn9i7H/nN5IhIcq8ZJSNPVR73FwIKlr5/p4rydWsOmTMGtTlWeTB+WXXWV5dsIu4xGTKebsxpVd1qvh6WjuWiOQi+tzOGfQ6iEi+sXcRLBsOMVfMPWpajDNPrGyTg77MNIzbCkaZKETZOUKVruBZ3NqPQrJJrhkyJiJ5k2EYfPrHEab8fgSAZhW9+eyJGrg56leOiIiIiORAMVdg2auwd6H5tk916Po1FKlg1VhpMpnMQ8TsncDZ2mEkN9NfZyKSpWITkhi5cDe/7DoLwMBHSjOqbSVNHC0iIiIiOdOxNbDkJbh2Fky20PhVaDwSbO2tnUwkW6kgJCJZ5nxkLM/O2cGuU1exszExsXNVetUpZe1YIiIiIiKpxUfD6rGw7Rvz7UJloctXUKKWdXOJPCAqCIlIlth3NoKBs7YTFhFLARd7pj9Vk/oPFbJ2LBERERGR1E7vgMXPwaWj5tt1noMW48HBxbq5RB4gFYRE5L6t2hfOsPk7iY5PokwRV4L61Ma/sKu1Y4mIiIiIpJSUAOs/hPUfgZEE7r7QaSqUbW7tZCIPnApCInLPDMPgy3XH+WDlQQwDHilbmGlPPoyni8Zbi4iIiEgOc+EQLHoOwnaab1ftDu0+Amcvq8YSsRYVhETknsQlJjF60V5++uc0AM/U82Nsh8rY2+agS3KKiIiIiCQnw9av4Pdx5suvOxWA9h9D1W7WTiZiVSoIiUimXboexwvf7WDbiSvYmODtDlXo08Df2rFERERERFKKOA1LBkHIOvPth5pDp2ng4WPdXCI5gApCIpIph89dY8CsbZy6HIO7ox3TnnqYxuWLWDuWiIiIiMh/DAN2z4flr0FcBNi7wGPvQK0BYDJZO51IjqCCkIhk2J+HzjN07r9ci0ukVEEXgvrWoqy3u7VjiYiIiIj8J+oS/DoMDiw13y5eC7p+DYUesmoskZxGBSERuSvDMJi58QQTl+0n2YA6pQvy5dM1KejqYO1oIiIiIiL/ObwSfh4MUefBxg4efQMavgK2+tNX5Hb6qRCRO0pISubtpfuYuyUUgMdrluB/XarhYKfJo0VEREQkh4i7DqvGwI5g8+0iFaHLV+Bb3ZqpRHI0FYREJF0R0QkMmruDjUcvYTLBqDYVebZRGUwady0iIlkkMjYBexsbnB1srR1FJOeIDIOfBwEmqPY4VOoAjm7WTpVzhW6Gxc/DlRPm2/VeguZjwd7JqrFEcjoVhEQkTccvXGfgrO0cvxiFi4Mtn/aqQcvKRa0dS0RE8pCfdpxm4rL9DHikNIOblbN2HJGc4cIh+K4bRJwy3z72BywbDhXbQUAvKPOohj/dlBgHayfBxk/BSAbPktD5Cyjd2NrJRHIF/SYRkVQ2Hb3Ii9//Q0RMAsULOPNtn1pU8vGwdiwREclj7GxNXIlO4Kt1x3mqrh9emptO8rvQLTC3B8RehUJloWp32LMALh8z/7tnAbh6Q7XuENADfKrn3ytmndsHi56Dc3vNtwOfhDbvgZOndXOJ5CKaBEREUpi7JZTeQVuJiEmgRqkCLHmpoYpBIiKSLToE+FLZx4NrcYlM+/OoteOIWNeBX2F2R3MxqERt6L8Kmo6CITtg4Bqo8zy4FDJPlrz5C/j6UZhWF9Z/BFdDrZ3+wUlOMvcI+vpRczHIpRD0/A66TFcxSCSTTIZhGNYO8SBFRkbi6elJREQEHh76I1fkpqRkg4nL9jNz4wkAOlX35f1uATjZa04HEbEefW7nDNn5Oqw9dJ6+M7fhYGvDmlebUMLLJUuPL5IrbJsBy181D3sq3wa6B4FDGj8LSQlwbA3s+gEOLYfE2P/u82sIAT2hcidwLvDAoj9QV07A4hchdJP5dvk20PEzcPO2aiyRnCQzn9kaMiYiXItNYMi8f1l76AIAI1qWZ3Czspo8WkREsl2T8kWoX6YQfx+/xCerjzC5R6C1I4k8OIYBaybCho/Mtx/uA+0+Tn+OIFt7KN/KvMRGwoGlsHs+hGyAkxvNy/KR5vsDe0HZlmCXB4ZiGgb8OwdWjIL46+DgBq0nQY1n8u+QOZEsoB5CIvncqcvR9A/expHz13Gyt+HjHtVpW83H2rFERAB9bucU2f067Dx1lc7TNmIywYqXG1OhmHuWn0Mkx0lKgF+Gwc7vzLebjoHGI++twBFxGvYsNBeHzu//b72zF1Tpau45VLJO7iyeXD8PS4fC4d/Mt0vVh87ToWBp6+YSyaEy85mtgpBIPrbtxGWen7ODy1HxFPVw5JvetQgoUcDasURELPS5nTM8iNfhxe928NvecFpU8ubbPrWz5RwiOUZ8FPzYB46uBpMttP8Eava5/+Mahnlend3zYfcCuB7+331e/ubCUEBPKPTQ/Z/rQTjwK/zyMkRfBFsHaPYm1B8MNprSQCQ9KgjdgRqWImY/7TjNqEV7iE9KpmpxD77tXZtink7WjiUikoI+t3OGB/E6HLtwncc+WU9SssGCF+pT279gtpxHxOquXzBfSezsP2DnDI8HQ4XWWX+e5CQIWW8uDu1fCglR/91XvJZ5SFmVruBaKOvPfb9iI+C3N2DXXPPtolWhy1dQrKp1c4nkApn5zNZVxkTymeRkg/dXHGTEgl3EJyXTukoxfny+vopBIiJiVQ8VcaNHrZIAvPfbQfLZd5aSX1w+DkGPmYtBzgWh76/ZUwwCcy+ah5pCly9h5BHo+q15TiGTLZzZbp7EenJ5mNsL9i6ChJjsyZFZIRtgesMbxSATNBwGz65RMUgkG2hSaZF8JCoukVfm72TV/nMADG5aluEty2NjkwvHk4uISJ4zrEU5Fv97mh0nr/D7gfO0rFzU2pFEss6Zf+D7x83Dnwr4wdOLoHDZB3NuB1cIeNy8XD8Pe38yX6ksbKd5bp7Dv4GjB1TuCAG9zFcss3nAfQcSYmHNO/D3VPNtL3/o/CX41X+wOUTyERWERPKJs1djGDhrO/vDInGwteH97tXoUqOEtWOJiIhYFPVwol/D0kxfe4wPVx6kWUVvbPWlheQFR36HH3ubh20VC4CnFoK7lQqebt5Q70XzcuHQjfmGfoSIU/Dvd+bFo8SNAlJP8K6U/ZnCdsGi5+DCQfPth/tAq/+BoyaYF8lOmkNIJB/Yeeoqz87ezoVrcRR2c+CrZ2pR08/L2rFERO5Kn9s5w4N8HSJiEmj8wZ9ExCTwYfcAHr8xjEwk19o5F5YOgeREKNMUes7JeYWO5GQI/dtcHNq3BOIi/ruvWDVzr6Fq3cG9WNaeNykRNn4Ca98zPz+u3tDx8+wbRieSD2gOIRGx+GXXWXp+9TcXrsVRoag7S15qqGKQiIjkWJ7O9rzU1HwFpE9WHyY2IcnKiUTukWHA+o9gyYvmYkdAT3jyx5xXDALz8DD/htDxM3j1MPSYDRXagY09hO+BVWPg40owp4t5qFnc9fs/56VjMLM1rJlofn4qdYBBf6sYJPIAaciYSB5lGAZTfj/Cp38cAaB5RW8+faIGbo76sRcRkZytd31/Zm48wdmIWOb8fZJnG5exdiSRzElOgt9eg23fmm83HAYtxoEpFwyBtHeCyp3MS/Rl2LfIPKTs1BY4tsa82LtAxfYQ2BNKPwq2mWhfGgZsnwGr3oKEaPPcRW0/NBfMcsPzI5KHaMiYSB4Um5DEqwt28evuMACebVSaN9pU0jwMIpLr6HM7Z7DG6/DjtlO89tNuCrjYs/61png42T+Q84rct4QYWPQsHPgFMEGb96Hu89ZOdf8uH4fdC2D3D+b/3+RWFKp2h4Ae4BN456JOZBgsHQxHfzff9m8EnadDAQ0NFckqmfnMVkFIJI85HxnLs7O3s+t0BHY2Jv7XpSo9a5eydiwRkXuiz+2cwRqvQ2JSMq0/3cDR89d5qelDjGxV8YGcV+S+RF+GH540z8dj6wBdv4Eqna2dKmsZBpzZYZ5vaO9PEH3pv/uKVDQXhqo9DgVua3/uXQTLhkPMFbB1NPeYqvvCg7+amUgep4LQHahhKXnZ3jMRPDt7O2ERsRRwsWf6UzWp/1Aha8cSEbln+tzOGaz1OqzaF85zc3bgZG/DupFNKerh9MDOLZJpV0/Bd93g4iFw9IQn5oL/I9ZOlb2SEuDoH+ZeQweXQ1Lcf/f5PWIuDpV5FP6YAHsXmtf7VIcuX4G3irwi2SEzn9maTEQkj1i5L5xhP+wkJiGJh4q4MqNPbfwLu1o7loiIyD1rWbkoNf282HHyCp/+cYR3u1SzdiSRtJ3bZy4GXQsDd194+icoWtnaqbKfrb15EugKrSE2AvYvNfccOvEXnLyx3GSyhUYjoMlr5v1ExOrUP08klzMMg+lrj/HCdzuISUiiUbnCLBrUUMUgEZE8av369XTo0AFfX19MJhNLliy54/aLFi2iZcuWFClSBA8PD+rXr8/KlSsfTNj7ZDKZeL21uRfB/G2nOH4hC65sJJLVQjZAUGtzMahIJRi4On8Ug27n5AkPPwN9f4VX9pqHhBWpZL6v4EMwYBU0G6NikEgOooKQSC4Wl5jEqwt28/6KgxgGPFPPj5l9a+PprA9aEZG8KioqisDAQKZOnZqh7devX0/Lli1Zvnw5O3bsoGnTpnTo0IF///03m5NmjTqlC9KsojdJyQaTVx22dhyRlPYugu+6Qlwk+DWE/r+BZwlrp7I+zxLwyCvmy8gP/df8b4la1k4lIrfRkDGRXOrS9Tien7OD7SevYGtj4u0Oleld39/asUREJJu1adOGNm3aZHj7KVOmpLj97rvv8vPPP/PLL79Qo0aNLE6XPV5rXYE/D51n2Z4wnjt1lcCSBawdSQQ2T4cVowADKnU0TyBtr3muUjCZoGAZa6cQkXSoh5BILnT43DU6TdvI9pNXcHeyY2bf2ioGiYhIhiQnJ3Pt2jUKFiyY7jZxcXFERkamWKypYjEPutQoDnCjV2y+uiaK5DTJybDqTVjxBmBAnefg8WAVg0Qk11FBSCSX+fPQebp+sYnTV2LwK+TC4kENaFy+iLVjiYhILjF58mSioqLo0aNHuttMmjQJT09Py1KyZMkHmDBtw1uWx8HWhk3HLrHhyEVrx5H8KjEeFj8Hmz43324xDtp8ADa2Vo0lInIvVBASySUMwyDorxAGBG/jelwidUoXZMmghpT1drd2NBERySXmzZvHuHHjmD9/Pt7e3uluN2rUKCIiIizLqVOnHmDKtJXwcuHpen6AuZdQcrJ6CckDFhsJ33eHPQvAxs586fRHXjEPixIRyYVUEBLJBRKSkhm9eC8Tft1PsgE9apXguwF18XJ1sHY0ERHJJebPn8+AAQP48ccfadGixR23dXR0xMPDI8WSEwxuVhY3Rzv2nY3kl91nrR1H8pNr4TCzLYSsA3tXePJHCOxl7VQiIvdFBSGRHO5qdDx9grYyb2soJhOMaVuJ97sF4GCnH18REcmYefPm0bdvX+bOnUu7du2sHeeeFXR14PnG5glqJ686THxispUTSb5w8Qh82xLO7QHXItBvGZRtbu1UIiL3TX9RiuRgIRej6PLFJjYdu4Srgy3fPFOLZxuXwaSuySIi+db169fZuXMnO3fuBCAkJISdO3cSGhoKmId79e7d27L9vHnz6N27N5MnT6ZevXqEh4cTHh5ORESENeLftwGNSlPYzZHQy9H8sC3U2nEkrzu1FWa0hIhQKPgQDFgNvrnj6nwiInejgpBIDnX2agxPfL2ZkItRFC/gzMIXG9CiclFrxxIRESvbvn07NWrUsFwyfvjw4dSoUYOxY8cCEBYWZikOAXz11VckJiby0ksv4ePjY1lefvllq+S/Xy4OdrzcvCwAn/1xhKi4RCsnkjzr4HKY1RFirkDxmjBgFRQsbe1UIiJZxmTks+t2RkZG4unpSURERI4ZDy9yu4iYBB7/chOHz12nrLcb856tRxF3R2vHEhF54PS5nTPktNchISmZlh+v48SlaF5pUZ6XW5SzdiTJa7bPhGXDwUiGcq3g8Zng4GrtVCIid5WZz2z1EBLJYeISk3h+znYOn7uOt7sjwf1qqxgkIiJyC3tbG0Y8VgGAr9cf49L1OCsnkjzDMODPd+HXYeZiUI2noddcFYNEJE9SQUgkB0lONhi5YDebj1/G1cGWmf1qU8LLxdqxREREcpx21XyoWtyDqPgkpv551NpxJC9ISoSlQ2Dd++bbTV6HjlPB1s66uUREsokKQiI5yPsrD7J011nsbEx8+UxNqvh6WjuSiIhIjmRjY+L11hUB+G7zSU5djrZyIsnV4qPghyfh3zlgsoH2U6DpaNCFPEQkD1NBSCSHmLXpBF+tOw7A+90CaFSuiJUTiYiI5GyNyhXhkbKFSUgy+Hj1YWvHkdwq6iLM6gBHVoKdE/T8Hmr1s3YqEZFsp4KQSA6wYm84437ZB8Crj5WnW80SVk4kIiKSO9zsJbRk5xkOhEVaOY3kOpdDYMZjcGYHOHtBn1+gYltrpxIReSBUEBKxsh0nr/DyD/9iGPBEnVK81LSstSOJiIjkGtVKeNIuwAfDgA9WHLR2HMlNzu40F4MuHwPPUtB/FZSsY+1UIiIPjApCIlZ0/MJ1Bs7aRlxiMs0qevNOpyqYNFZdREQkU159rAJ2Nib+PHSBzccvWTuO5AZH/4DgdhB1HopWgwGroEh5a6cSEXmgVBASsZIL1+LoM3MrV6ITCCjhydQna2Bnqx9JERGRzCpd2JVedUoC8N5vBzEMw8qJJEfb9QPM7QHx16F0E+i3HDx8rJ1KROSB01+fIlYQHZ/IgFnbOHU5hlIFXZjRpzYuDrqkqYiIyL0a2rwczva27Dx1lZX7zlk7juREhgF/fQKLn4fkRKjaHZ5aCE4e1k4mImIVKgiJPGCJSckMnvsvu09H4OViT3C/2hRxd7R2LBERkVzN292JAY+UBuDDlQdJTEq2ciLJUZKT4LfX4Pdx5tsNhkDXb8DOwaqxRESsSQUhkQfIMAze+nkvaw6ex9HOhhl9a1OmiJu1Y4mIiOQJzzUpg5eLPccuRPHTP6etHUdyioRYWNgPtn5tvt1qEjw2EWz0p5CI5G/6LSjyAE1dc5R5W09hY4LPn6jBw6W8rB1JREQkz/BwsrdcrfOT1UeITUiyciKxupgr8F1X2P8z2DpA9yCoP8jaqUREcgQVhEQekAXbTzF59WEAxnWswmNVilk5kYiISN7zdD0/ihdwJjwyluBNJ6wdR6wp4jQEtYGTG8HRA57+Cap2s3YqEZEcQwUhkQdg/eELjFq0B4AXmjxE7/r+1g0kIiKSRznZ2/JKS/Plw7/48ygR0QlWTiRWcW4/fNsSLhwAdx/o9xuUbmztVCIiOYoKQiLZbN/ZCF78bgeJyQadqvvyWqsK1o4kIiKSp3WpUZwKRd2JjE1k+rpj1o4jD1J8lHl4WFBruHYWCleAAauhWFVrJxMRyXF0nWuRbHT6SjT9Zm4jKj6J+mUK8UH3AGxsTNaOJSIikjOcPwgh68GlIDgXAGev/xZHz3ue9NfWxsTIVhUYOHs7MzeG0KeBHz6ezlmbXXKGuGsQugVO/gUnNsLZf8yXlAcoWQ+emGd+f4mISCoqCIlkk4joBPrO3Mb5a3FUKOrOl8/UxNHO1tqxREREco7Qv+G3kencaUpdJEqxFEx7vZMn2NrRvJI3tf292HbiCp/+foT3ugU8yEcm2SXmKoRu/q8AFLYLjNsmD/coAZXaQ4txYK9CoIhIelQQEskGsQlJPDtnO0fPX6eYhxMz+9XG09ne2rFERERyFs+SULmT+UpQMVfMf+zHXIH464Dx3/rMcvTE5FyAYFsPdthDxE5XIpLL41nQ+0ZvpLQKSQXAziGLH6Dct+jL5sLhiY1wYgOE7wGMlNsU8AP/R8CvIfg3NN82qUe2iMjdqCAkksWSkw1GLNjF1pDLuDvaEdy/Nr4F9O2UiIhIKuVamJfbJcbfUiTK6HIV4iLM+8dFQFwErkDjm51z922+ex4HtxsFogIZ6410c7F3yprnQyDqovmqYCc2mv89t49UBaCCD5kLP36PmP/1LGGVqCIiuZ0KQiJZ7N3lB1i2Owx7WxNfPVOTisU8rB1JREQkd7FzAPei5iUzkhIhNgJiLlsKRWHhZ/lm1Q48ieKJau5420WnXUzCMPdMir8OEacymdcZXApBoTJQuPyNpZz5X4/i6q1yJ9fO3SgA/WX+98LB1NsULn+j98+NXkAePg8+p4hIHqSCkEgWmvFXCN/+FQLAR48H0qBsYSsnEhERyUds7cC1kHm5wac8XDv3MEE7TrPpakF+eK4eptsLNMnJ5l5F0Zf/G7aW0cVIgsQYiDxtXkLWpzy2vSsULpu6UFTwofzZsyjy7I3ePzfmALp0JPU23pX/G/7l1xDcvB98ThGRfEAFIZEssnxPGBOX7Qfg9dYV6VS9uJUTiYiICMArLcvz866zbAm5zNrDF2ha4bYCg43Nf8O/MsMwzFe5irkC18/DpaNw8fCN5QhcPgYJUeaJj8N23bazCbz8UheKCpc39zbKK72KroamLABdCbltAxMUrfpf8cevYYqCnoiIZB8VhESywLYTlxk2fyeGAc/U8+OFJmWsHUlERERu8C3gTJ/6fnyzIYT3fztIk3JFsLHJgoKLyQROHubFyw9K1k55f1ICXDl5o0B0yFwkulkwio2AKyfMy5FVKfdz9kpdJCpc3jxZsm0Obr4bhvnxWOYA+stcELqVyQaKBfw3/MuvfuYLcSIikiVy8CeKSO5w9Px1Bs7aTnxiMi0rF2Vcxyqpu6KLiIiIVQ16tCw/bD3FwfBr/LzrDF1qPICJiG3tbwwXKwu0/W+9YUDUhZS9iW7+/+opc4+jU1vMS4rjOZiHmqUoFJUzL47u2f94bmcYcOnYf71/Tm6EyDMptzHZgm/1G0PAGkGpuuDk+eCziohIKioIidyH89di6RO0lYiYBGqUKsBnvWpgmxXfOIqIiEiW8nJ14IVHH+LDlYeYvOowbav54Ghne/cds4PJZJ4Xx83b3FPmVvHR5qFmtxeKLh41z1V04YB5uZ27bxqFovLg4Zt1w88Mw5zl5gTQJzbC9fCU29jYQ/GH/5sDqGRd6xSrRETkrlQQErlH1+MS6R+8jTNXYyhd2JUZfWrj7GClhqWIiIjcVf+GpZm16QSnr8Qwd0so/RqWtnak1BxcoFg183Kr5GTzpNUpCkU3/r1+Dq6dNS8h6247nhsUSmNS60IPgZ3jnbMkJ5uLTzeHf53cZO7ZdCtbByhey1z88X8EStQxPwYREcnxVBASuQcJSckM+v4f9p6JpJCrA8H9alPQ1cHasUREROQOnB1seblFOcYs3svna47SvWYJ3J3srR0rY2xsoEAp81K2Rcr7Yq7e1pvoxv8vH4f46xC207zcymRjnpPo9rmK7J0gdPONXkCbIOZyyv3snKBE7f/mACpRC+yds/GBi4hIdlFBSCSTDMNg9KI9rD98AWd7W4L61savkKu1Y4mIiEgG9KhVkm83hBByMYpvNoQwvGV5a0e6f84FzBNa3z6pdWK8eZLnVHMVHYG4CPMVv66EwJGV6R/b3sU87Mu/Ifg9Yh4OdreeRSIikiuoICSSSVN+P8KCHaexMcHUJ2sQWLKAtSOJiIhIBtnb2jCyVQUGff8P3244zjP1/CjinkcLHHYOUKS8ebmVYcD18+kXikrUvjEH0CPgW8M8ObaIiOQ5KgiJZML8baF8+scRAN7pXJXmlYpaOZGIiIhkVpuqxQgs4cmu0xFMXXOE8Z2qWjvSg2UygXtR81K6kbXTiIiIldhYO8AXX3xB6dKlcXJyombNmmzYsOGO23///fcEBgbi4uKCj48P/fr149KlSw8oreRnfx46z+jFewEY3LQsT9X1s3IiERERuRcmk4nXW1cEYO7WUE5eirJyIhERkQfPqgWh+fPnM2zYMMaMGcO///5Lo0aNaNOmDaGhoWlu/9dff9G7d28GDBjAvn37WLBgAdu2bWPgwIEPOLnkN3tOR/DS9/+QlGzQ9eHijHgsD8w3ICIiko81KFuYRuUKk5BkMHnVYWvHEREReeCsWhD6+OOPGTBgAAMHDqRSpUpMmTKFkiVLMn369DS337x5M/7+/gwdOpTSpUvzyCOP8Pzzz7N9+/YHnFzyk1OXo+kXvI3o+CQeKVuY97oGYDKZrB1LRERE7tPNXkJLd51l75kIK6cRERF5sKxWEIqPj2fHjh089thjKdY/9thjbNq0Kc19GjRowOnTp1m+fDmGYXDu3DkWLlxIu3bt0j1PXFwckZGRKRaRjLoSFU+fmVu5eD2OSj4eTH/6YRzsrD7SUkRERLJA1eKedAz0BeCDlYesnEZEROTBstpfthcvXiQpKYmiRVNOylu0aFHCw8PT3KdBgwZ8//339OzZEwcHB4oVK0aBAgX4/PPP0z3PpEmT8PT0tCwlS5bM0scheVdsQhIDZ2/n+IUofD2dCO5XG3cnXWVDRESsa/369XTo0AFfX19MJhNLliy56z7r1q2jZs2aODk5UaZMGb788svsD5pLjHisPHY2JtYfvsCmoxetHUdEROSBsXpXh9uH3hiGke5wnP379zN06FDGjh3Ljh07WLFiBSEhIbzwwgvpHn/UqFFERERYllOnTmVpfsmbkpINXv7hX3acvIKHkx3B/etQ1MPJ2rFERESIiooiMDCQqVOnZmj7kJAQ2rZtS6NGjfj3338ZPXo0Q4cO5aeffsrmpLmDXyFXnqxbCoD3VxzEMAwrJxIREXkwrHbZ+cKFC2Nra5uqN9D58+dT9Rq6adKkSTRs2JCRI0cCEBAQgKurK40aNWLixIn4+Pik2sfR0RFHR8esfwCSZxmGwTu/7mflvnM42Nrwde9alC/qbu1YIiIiALRp04Y2bdpkePsvv/ySUqVKMWXKFAAqVarE9u3b+eijj+jWrVs2pcxdhjQrx8Idp9l1OoLf9obTtlrqNqWIiEheY7UeQg4ODtSsWZPVq1enWL969WoaNGiQ5j7R0dHY2KSMbGtrC6BvcyTLfLshhOBNJwCY3COQemUKWTeQiIjIffj7779TzdnYqlUrtm/fTkJCQpr75Lc5GIu4OzKwURkAPlp5iMSkZCsnEhERyX5WHTI2fPhwvv32W4KCgjhw4ACvvPIKoaGhliFgo0aNonfv3pbtO3TowKJFi5g+fTrHjx9n48aNDB06lDp16uDr62uthyF5yNJdZ/nf8gMAjGlbiQ6Bel+JiEjuFh4enuacjYmJiVy8mPacOflxDsZnG5WmoKsDxy9G8eP209aOIyIiku0yXRDy9/dnwoQJhIaG3vfJe/bsyZQpU5gwYQLVq1dn/fr1LF++HD8/PwDCwsJSnKdv3758/PHHTJ06lapVq/L4449ToUIFFi1adN9ZRDYfv8SrP+4CoG8DfwY2Km3lRCIiIlkjrTkb01p/U36cg9HdyZ7BTcsCMOX3w8TEJ1k5kYiISPbKdEFoxIgR/Pzzz5QpU4aWLVvyww8/EBcXd88BBg0axIkTJ4iLi2PHjh00btzYcl9wcDBr165Nsf2QIUPYt28f0dHRnD17lu+++47ixYvf8/lFAA6fu8Zzs7cTn5RMm6rFeKt95XQbySIiIrlJsWLF0pyz0c7OjkKF0h4W7ejoiIeHR4olP3iqXilKeDlz/locQRtDrB1HREQkW2W6IDRkyBB27NjBjh07qFy5MkOHDsXHx4fBgwfzzz//ZEdGkWx1LjKWvkFbiYxNpJafF5/0rI6tjYpBIiKSN9SvXz/VnI2rVq2iVq1a2NvbWylVzuRoZ8uIx8oD8OW6Y1yNjrdyIhERkexzz3MIBQYG8umnn3LmzBnefvttvv32W2rXrk1gYCBBQUGa5FlyhWuxCfQJ2srZiFjKFHHlm961cLK3tXYsERGRdF2/fp2dO3eyc+dOwHxZ+Z07d1qG2d8+B+MLL7zAyZMnGT58OAcOHCAoKIgZM2bw6quvWiN+jtcpsDgVi7lzLTaRL9Yes3YcERGRbHPPBaGEhAR+/PFHOnbsyIgRI6hVqxbffvstPXr0YMyYMTz11FNZmVMky8UnJvPid/9wMPwahd0cmdWvDl6uDtaOJSIickfbt2+nRo0a1KhRAzBfpKNGjRqMHTsWSD0HY+nSpVm+fDlr166levXqvPPOO3z22We65Hw6bGxMvN66IgDBm05w5mqMlROJiIhkD5ORya48//zzDzNnzmTevHnY2tryzDPPMHDgQCpWrGjZZtu2bTRu3JiYmJz3ARoZGYmnpycRERH5Zjy8pGYYBiN+3MWif8/g4mDL/OfqU62Ep7VjiYjIbfS5nTPkt9fBMAx6fb2ZLSGXebxmCT58PNDakURERDIkM5/Zme4hVLt2bY4cOcL06dM5ffo0H330UYpiEEDlypXp1atXZg8t8sBMXnWYRf+ewdbGxLSnHlYxSERERCxMJhOvtzG3b3/65zRHzl2zciIREZGsZ5fZHY4fP265LHx6XF1dmTlz5j2HEslO3285ydQ/jwLwbpeqNK3gbeVEIiIiktM8XMqLVlWKsnLfOT5YeYhveteydiQREZEslekeQufPn2fLli2p1m/ZsoXt27dnSSiR7PLHgXO8tWQvAC83L0fP2qWsnEhERERyqpGtKmBjgtX7z7H9xGVrxxEREclSmS4IvfTSS5w6dSrV+jNnzvDSSy9lSSiR7LDz1FUGz/2XZAN61CrBsBblrB1JREREcrCy3u70qFUSgPdXHNRVdEVEJE/JdEFo//79PPzww6nW16hRg/3792dJKJGsdvJSFAOCtxGTkEST8kX4X5dqmEwma8cSERGRHG5Yi/I42tmw7cQV1hw8b+04IiIiWSbTBSFHR0fOnTuXan1YWBh2dpmekkgk2126HkefoK1cioqnanEPvnjqYextM/3WFxERkXyomKcTfRv6A/DBikMkJauXkIiI5A2Z/qu4ZcuWjBo1ioiICMu6q1evMnr0aFq2bJml4UTuV0x8EgNmbefEpWhKeDkT1Lc2ro4qXIqIiEjGDWpSFg8nOw6du8bif89YO46IiEiWyHRBaPLkyZw6dQo/Pz+aNm1K06ZNKV26NOHh4UyePDk7Morck6RkgyHz/mXnqat4OtsT3K8O3u5O1o4lIiIiuYyniz2DmpYF4JPVh4lNSLJyIhERkfuX6YJQ8eLF2b17Nx988AGVK1emZs2afPrpp+zZs4eSJUtmR0aRTDMMg7eX7uX3A+dwsLPh2z61KOvtZu1YIiIikkv1beBPMQ8nzlyN4bvNJ60dR0RE5L7d09gZV1dXnnvuuazOIpJlZm06wXebQzGZ4NOe1antX9DakURERCQXc7K3ZViLcryxaA/T/jxKj9ol8XCyt3YsERGRe3bPk6ns37+f0NBQ4uPjU6zv2LHjfYcSuR8hF6OY9NtBAMa0rUSbaj5WTiQiIiJ5QfeaJfhmw3GOXYji63XHebVVBWtHEhERuWeZLggdP36cLl26sGfPHkwmE4ZhvtLCzUt4JyVpTLVYT3KywesLdxOXmMwjZQsz4JHS1o4kIiICwKlTpzCZTJQoUQKArVu3MnfuXCpXrqye17mEna0NI1tV4IXv/mHGXyH0ru+Ht4fmJxQRkdwp03MIvfzyy5QuXZpz587h4uLCvn37WL9+PbVq1WLt2rXZEFEk4+ZsPsnWE5dxcbBlUtdqlkKliIiItT355JP8+eefAISHh9OyZUu2bt3K6NGjmTBhgpXTSUa1qlKM6iULEJOQxGdrjlg7joiIyD3LdEHo77//ZsKECRQpUgQbGxtsbGx45JFHmDRpEkOHDs2OjCIZcupyNO+vMA8Ve6NNRUoWdLFyIhERkf/s3buXOnXqAPDjjz9StWpVNm3axNy5cwkODrZuOMkwk8nEG20qAvDD1lOEXIyyciIREZF7k+mCUFJSEm5u5qs1FS5cmLNnzwLg5+fHoUOHsjadSAYZhsHrP+0mOj6JuqUL8nRdP2tHEhERSSEhIQFHR0cAfv/9d8u8ixUrViQsLMya0SST6pUpxKMVipCYbPDRKrV/RUQkd8p0Qahq1ars3r0bgLp16/LBBx+wceNGJkyYQJkyZbI8oEhGzN0ayqZjl3Cyt+H9bgHY2GiomIiI5CxVqlThyy+/ZMOGDaxevZrWrVsDcPbsWQoVKmTldJJZr7WqiMkEy3aHsed0hLXjiIiIZFqmC0JvvvkmycnJAEycOJGTJ0/SqFEjli9fzmeffZblAUXu5szVGCYtNw8VG9mqIv6FXa2cSEREJLX333+fr776ikcffZQnnniCwMBAAJYuXWoZSia5R2VfDzpXLw5gGbIuIiKSm5iMm5cJuw+XL1/Gy8srV0zgGxkZiaenJxEREXh4eFg7jtwnwzDoM3Mb6w9foKafFz8+Xx9b9Q4SEckz8trndlJSEpGRkXh5eVnWnThxAhcXF7y9va2Y7M7y2uuQVU5djqbZ5LUkJBnMGVCHRuWKWDuSiIjkc5n5zM5UD6HExETs7OzYu3dvivUFCxbMFcUgyXsW7DjN+sMXcLAzDxVTMUhERHKqmJgY4uLiLMWgkydPMmXKFA4dOpSji0GSvpIFXXjqxryF7684SHLyfX/PKiIi8sBkqiBkZ2eHn58fSUlJ2ZVHJMPCI2J559f9AAxvWZ6y3m5WTiQiIpK+Tp06MXv2bACuXr1K3bp1mTx5Mp07d2b69OlWTif3anCzsrg62LL3TCQ/bDtl7TgiIiIZdk9zCI0aNYrLly9nRx6RDDEMgzGL93AtNpHAEp4MfKS0tSOJiIjc0T///EOjRo0AWLhwIUWLFuXkyZPMnj1b8zDmYoXdHHmpWVkAxv68lz8PnbdyIhERkYyxy+wOn332GUePHsXX1xc/Pz9cXVNO4PvPP/9kWTiR9CzZeYY/Dp7HwdaGDx8PxM4207VNERGRByo6Ohp3d3cAVq1aRdeuXbGxsaFevXqcPHnSyunkfrzQ+CEOh19jyc6zDPruH75/ti4Pl/K6+44iIiJWlOmCUOfOnbMhhkjGnb8Wy7il5qFiQ5uXpXxRdysnEhERubuyZcuyZMkSunTpwsqVK3nllVcAOH/+vCZqzuVsbEx80D2Qy9EJrD98gf7B21j4Qn3KequNIiIiOVemC0Jvv/12duQQyRDDMBi7ZB8RMQlU8fXg+SYPWTuSiIhIhowdO5Ynn3ySV155hWbNmlG/fn3A3FuoRo0aVk4n98vBzobpTz3Mk99sZtfpCHrP2MpPgxrg4+ls7WgiIiJp0jgbyVWW7Qljxb5w7GxMfNg9EHsNFRMRkVyie/fuhIaGsn37dlauXGlZ37x5cz755BMrJpOs4upoR1Df2pQp7MrZiFj6BG0lIjrB2rFERETSlOm/pm1sbLC1tU13Eckul67HMfbnfQAMalqWyr7qXi8iIrlLsWLFqFGjBmfPnuXMmTMA1KlTh4oVK1o5mWSVQm6OzB5Qh6Iejhw+d50Bs7YRE68r9IqISM6T6YLQ4sWLWbRokWWZP38+b7zxBj4+Pnz99dfZkVEEgLeX7uNyVDwVi7kzuGlZa8cRERHJlOTkZCZMmICnpyd+fn6UKlWKAgUK8M4775CcnGzteJKFSni5MKt/Hdyd7Nh+8gpD5v1DYpJeYxERyVkyPYdQp06dUq3r3r07VapUYf78+QwYMCBLgoncasXecH7dHYbtjaFiDnYaKiYiIrnLmDFjmDFjBu+99x4NGzbEMAw2btzIuHHjiI2N5X//+5+1I0oWqljMgxl9avP0jC38fuA8oxfv4f1uAZhMJmtHExERAbJwDqG6devy+++/Z9XhRCyuRsfz5pK9ADzfuAzVSnhaOZGIiEjmzZo1i2+//ZYXX3yRgIAAAgMDGTRoEN988w3BwcHWjifZoE7pgkx9ogY2Jvhx+2k+WnXI2pFEREQssqQgFBMTw+eff06JEiWy4nAiKUz4ZT8Xr8dR1tuNoc3LWTuOiIjIPbl8+XKacwVVrFiRy5cvWyGRPAiPVSnGu12qATDtz2PM3Bhi5UQiIiJmmR4y5uXllaKrq2EYXLt2DRcXF7777rssDSey5uA5Fv17BhsTfNA9ACd7TVwuIiK5U2BgIFOnTuWzzz5LsX7q1KkEBARYKZU8CL3qlOLi9Tg+WnWY8b/sp5CbIx0Dfa0dS0RE8rlMF4Q++eSTFAUhGxsbihQpQt26dfHy8srScJK/RcQkMGrRHgAGPFKah0vp/SUiIrnXBx98QLt27fj999+pX78+JpOJTZs2cerUKZYvX27teJLNXmpalgvX4pj190lG/LgTLxd7GpUrYu1YIiKSj2W6INS3b99siCGS2rvLDnAuMo7ShV0Z8VgFa8cRERG5L02aNOHw4cNMmzaNgwcPYhgGXbt25bnnnmPcuHE0atTI2hElG5lMJsZ2qMLFqHiW7Q7jhTk7mPdcPQJKFLB2NBERyadMhmEYmdlh5syZuLm58fjjj6dYv2DBAqKjo+nTp0+WBsxqkZGReHp6EhERgYeHh7XjSDrWH75A76CtmEzw4/P1qe1f0NqRRETECvLD5/auXbt4+OGHSUpKsnaUdOWH1+FBiUtMon/wNjYevUQhVwcWvtiA0oVdrR1LRETyiMx8Zmd6Uun33nuPwoULp1rv7e3Nu+++m9nDiaRyPS7RMlSsT31/FYNERERu88UXX1C6dGmcnJyoWbMmGzZsuOP233//PYGBgbi4uODj40O/fv24dOnSA0ort3K0s+XLp2tStbgHl6LieWbGFs5Hxlo7loiI5EOZLgidPHmS0qVLp1rv5+dHaGholoSS/G3S8gOcuRpDyYLOvNZaQ8VERERuNX/+fIYNG8aYMWP4999/adSoEW3atEm3HfbXX3/Ru3dvBgwYwL59+1iwYAHbtm1j4MCBDzi53OTuZM/MvnXwK+TC6Ssx9A7aSmRsgrVjiYhIPpPpgpC3tze7d+9OtX7Xrl0UKlQoS0JJ/rXp6EW+32Ju0L7fLQAXh0xPcyUiIpKnffzxxwwYMICBAwdSqVIlpkyZQsmSJZk+fXqa22/evBl/f3+GDh1K6dKleeSRR3j++efZvn37A04utyri7sic/nUp7ObIwfBrPDtrO7EJOXfYoIiI5D2Z/mu7V69eDB06FHd3dxo3bgzAunXrePnll+nVq1eWB5T8Izo+kdcXmYuNT9UtRYOHUg9NFBERyW26du16x/uvXr2a4WPFx8ezY8cO3njjjRTrH3vsMTZt2pTmPg0aNGDMmDEsX76cNm3acP78eRYuXEi7du3SPU9cXBxxcXGW25GRkRnOKBlXqpALs/rXpudXm9kScplhP+xk2lMPY2tjuvvOIiIi9ynTPYQmTpxI3bp1ad68Oc7Ozjg7O/PYY4/RrFkzzSEk9+WDFYc4dTmG4gWcGdW2krXjiIiIZAlPT887Ln5+fvTu3TtDx7p48SJJSUkULVo0xfqiRYsSHh6e5j4NGjTg+++/p2fPnjg4OFCsWDEKFCjA559/nu55Jk2alCJjyZIlM/6AJVOq+Hryde+aONjasGJfOG/9vJdMXvNFRETknmS6h5CDgwPz589n4sSJ7Ny5E2dnZ6pVq4afn1925JN8YtuJy8z6+wQA73athpujhoqJiEjeMHPmzCw/psmUsgeJYRip1t20f/9+hg4dytixY2nVqhVhYWGMHDmSF154gRkzZqS5z6hRoxg+fLjldmRkpIpC2ajBQ4WZ0qs6L839h7lbQini5sgrLctbO5aIiORx9/xXd7ly5ShXrlxWZpF8KiY+idcW7sYwoEetEjQpX8TakURERHKkwoULY2trm6o30Pnz51P1Grpp0qRJNGzYkJEjRwIQEBCAq6srjRo1YuLEifj4+KTax9HREUdHx6x/AJKuttV8mNCpKm8t2cunfxyhsLsjz9TTF64iIpJ9Mj1krHv37rz33nup1n/44Yc8/vjjWRJK8pePVx8i5GIURT0cGdOusrXjiIiI5FgODg7UrFmT1atXp1i/evVqGjRokOY+0dHR2NikbPLZ2toCaGhSDvNMPT+GNjd/4Tr2570s3xNm5UQiIpKXZbogtG7dujQnIWzdujXr16/PklCSf/wTeoUZf4UAMKlrNTyd7a2cSEREJGcbPnw43377LUFBQRw4cIBXXnmF0NBQXnjhBcA83OvWOYk6dOjAokWLmD59OsePH2fjxo0MHTqUOnXq4Ovra62HIel4pUU5nqxbCsOAYT/s5O9jl6wdSURE8qhMDxm7fv06Dg4Oqdbb29vrChSSKbEJ5qFiyQZ0rVGcZhXT7uouIiIi/+nZsyeXLl1iwoQJhIWFUbVqVZYvX26ZzzEsLIzQ0FDL9n379uXatWtMnTqVESNGUKBAAZo1a8b7779vrYcgd2AymXinU1UuX49nxb5wnpu9nR+er0cVX09rRxMRkTzGZGSyr3Dt2rXp0KEDY8eOTbF+3Lhx/PLLL+zYsSNLA2a1yMhIPD09iYiIwMPDw9px8rUPVhzki7XHKOzmyO/DG1PAJXWhUURE8jd9bucMeh0evNiEJHoHbWVryGUKuzmy6MUGlCrkYu1YIiKSw2XmMzvTPYTeeustunXrxrFjx2jWrBkAf/zxB3PnzmXhwoX3lljynT2nI/hq/XEAJnauqmKQiIiIyC2c7G35pncten71NwfDr/FM0BYWvtCAIu6a7FtERLJGpucQ6tixI0uWLOHo0aMMGjSIESNGcObMGdasWYO/v382RJS8Jj4xmZELd5GUbNA+wIfWVYtZO5KIiIhIjuPpbM/s/nUo4eXMyUvR9AveyvW4RGvHEhGRPCLTBSGAdu3asXHjRqKiojh69Chdu3Zl2LBh1KxZM6vzSR409c+jHAy/RiFXB8Z3rGLtOCIiIiI5lreHE3MG1KWQqwN7z0Ty/JztxCUmWTuWiIjkAfdUEAJYs2YNTz/9NL6+vkydOpW2bduyffv2rMwmedD+s5F88edRAMZ3qkIhN3V7FhEREbmT0oVdmdmvNi4Otmw8eokRP+4iOTlT04CKiIikkqk5hE6fPk1wcDBBQUFERUXRo0cPEhIS+Omnn6hcuXJ2ZZQ8IiHJPFQsMdmgVZWitKvmY+1IIiIiIrlCQIkCfPVMTfoHb+PX3WEUcnVgXMcqmEwma0cTEZFcKsM9hNq2bUvlypXZv38/n3/+OWfPnuXzzz/PzmySx3y17hj7zkZSwMWedzpXVQNGREREJBMalSvC5B7VAZj198n/t3ff4VGVeRvHvzOTZFJIJSSEBJLQO0iCSLOhKCiKoliQallE3UVe3ZV1bawrll1WXRcUBbHDuoqyCmoUparUANKlJUBCCOm9zHn/OBCIFEGSnEnm/lzXXMk5OTNzDyfkPPObpzD9u13WBhIRkXrtrHsIffXVV/z+97/n3nvvpU2bNrWZSRqgHYfyefkbc6jYE0M6EhHoa3EiERERkfrnum7NOFJQylP/28ILX26ncYAPt17YwupYIiJSD511D6Fly5aRn59PYmIivXr14pVXXuHw4cO1mU0aiIpKFw9/uIGyShcD2kcwtHu01ZFERERE6q2xfeOZcGkrAP48fxNJWw5ZnEhEROqjsy4I9e7dm9dff520tDR+97vfMXfuXKKjo3G5XCQlJZGfn1+bOaUem7V8Dxv25xLo68XfbuiioWIiIiIi5+nhq9oxPDEGlwH3v7+OVXuyrI4kIiL1zDmvMubv78+4ceNYvnw5mzZt4v/+7/949tlniYiI4LrrrquNjFKP7TpcwD+SdgDw2DUdaRqsoWIiIiIi58tms/HMDV24okMEpRUu7nprNdvS86yOJSIi9chvXnYeoF27djz//PPs37+fDz74oKYySQNR6TL44383Ulbh4uK2Tbg5McbqSCIiIiINhpfDzr9u60FibCh5JRWMnr2K/dlFVscSEZF64rwKQsc4HA6GDh3KggULauLhpIGYs3Iva/dl08jpxdQbNVRMREREpKb5+Th4Y3QibSMbcSivlFGzV5FVWGZ1LBERqQdqpCAk8kt7Mwt54cttAEwe3J7oED+LE4mIiIg0TCH+Prw17kKaBfuy+3AhY+espqiswupYIiLi5lQQkhrnchn86aONlJS76NOqMbdrKVQRERGRWhUV7Mfbd15IiL83G1JzuPfddZRXuqyOJSIibkwFIalx7/24jx/3ZOHn7eDZG7tqqJiIiIhIHWgdEcibY3ri5+1gyY7D/PG/G3G5DKtjiYiIm1JBSGpUalYRUxeZQ8X+dHU7WjT2tziRiIiIiOe4oEUo0+/ogcNuY/76A0xdtNXqSCIi4qZUEJIaYxgGkz/eRFFZJRfGhTGqd5zVkUREREQ8zmXtInh+WFcAXl+2h5lLd1mcSERE3JEKQlJj5q5OZfnPmTi97Dx3U1fsdg0VExEREbHCsIQY/jy4PQDPLNzGR2v3W5xIRETcjQpCUiMO5hTzt8/NLskPX9WO+PAAixOJiIiIeLZ7Lm7FPRe3BOCPH21k8bZDFicSERF3ooKQnDfDMPjz/E0UlFZwQYsQxvaNtzqSiIiIiACPXN2eGy+IptJlMOG9daxLybY6koiIuAkVhOS8fbTuAN9tP4yPl50XbuqKQ0PFRERERNyC3W7juZu6cmm7JpSUuxg3ZzU/Z+RbHUtERNyACkJyXg7llTDlf5sBmHhFG1pHBFqcSERERERO5O2wM31ED7o3DyGnqJxRs1aRlltsdSwREbGYCkLymxmGwaPzfyKvpIKuMcHc07+l1ZFERERE5BT8fbx4c0xPWjUJ4GBuCaNmrSKnqMzqWCIiYiEVhOQ3W7DhIF9vPYS3w8bzN3XFy6FfJxERERF3FRrgw9t39qJpkC87Mwq48601FJdVWh1LREQsYvk7+OnTpxMfH4+vry8JCQksW7bstMeOGTMGm8120q1Tp051mFgADueX8sQCc6jY/Ze1oX3TIIsTiYiIiMiviQ7x461xFxLk68Xafdnc//46KipdVscSERELWFoQmjdvHhMnTuTRRx9l/fr19O/fn0GDBpGSknLK41966SXS0tKqbqmpqYSFhXHzzTfXcXJ5YsFP5BSV0yEqiAmXtbI6joiIiIicpXZNA5k1pidOLzvfbMtg8sebMAzD6lgiIlLHLC0ITZs2jTvvvJO77rqLDh068OKLL9K8eXNmzJhxyuODg4Np2rRp1W3NmjVkZ2czduzYOk7u2RZuSmPhpnS87DZeuKkr3hoqJiIiIlKv9IwL49+398Bht/Hh2v288OV2qyOJiEgds+ydfFlZGWvXrmXgwIHV9g8cOJCVK1ee1WPMmjWLK664gtjY2NMeU1paSl5eXrWb/HZZhWU89slPANx7aSs6RwdbnEhEREREfosrOkYy9YYuAEz/bhezl++xOJGIiNQlywpCmZmZVFZWEhkZWW1/ZGQk6enpv3r/tLQ0Fi1axF133XXG46ZOnUpwcHDVrXnz5ueV29M9uWAzRwrLaBvZiPsvb211HBERERE5D8N7Nufhq9oBMOWzLXyafMDiRCIiUlcsH+tjs9mqbRuGcdK+U5kzZw4hISEMHTr0jMdNnjyZ3Nzcqltqaur5xPVoX21OZ8GGg9ht8MJN3XB6OayOJCIiIiLnacKlrRjTJw6Ahz7coKKQiIiH8LLqicPDw3E4HCf1BsrIyDip19AvGYbB7NmzGTlyJD4+Pmc81ul04nQ6zzuvp8stKucvR4eK3X1xS7o1D7E2kIiIiIjUCJvNxuPXdiSnqIxPkg/yh7nJ7M8uZsKlrc7qg1oREamfLOsh5OPjQ0JCAklJSdX2JyUl0adPnzPed8mSJfz888/ceeedtRlRTjDlsy1k5JfSskkAD17R1uo4IiIiIlKD7HYb04Z35+7+8QC88OV2Jn+8iXItSS8i0mBZOmRs0qRJvPHGG8yePZutW7fy4IMPkpKSwvjx4wFzuNeoUaNOut+sWbPo1asXnTt3ruvIHunb7Rl8tG4/Nhu8cFNXfL01VExERESkobHbbTx6TUemXN8Juw3mrk5l3JzV5JeUWx1NRERqgWVDxgBuueUWjhw5wpQpU0hLS6Nz584sXLiwatWwtLQ0UlJSqt0nNzeXjz76iJdeesmKyB4nr6ScyR9tAmBc33gSYsMsTiQiIiIitWlU7ziiQ/y4//31LNuZyc2vfs+bY3sSFexndTQREalBlk8qPWHCBPbu3UtpaSlr167l4osvrvrZnDlz+O6776odHxwcTFFREXfffXcdJ/VMUxduJT2vhNjG/jw0sJ3VcURERASYPn068fHx+Pr6kpCQwLJly854fGlpKY8++iixsbE4nU5atWrF7Nmz6yit1EcDOkTyn9/1pkmgk23p+Qz99wo2H8y1OpaIiNQgywtC4r6W78zkg1XmqmzPDeuKn4+GiomIiFht3rx5TJw4kUcffZT169fTv39/Bg0adFKv6hMNHz6cb775hlmzZrF9+3Y++OAD2rdvX4eppT7qEhPM/Al9aBvZiEN5pQx/9Xu+3Z5hdSwREakhNsMwDKtD1KW8vDyCg4PJzc0lKCjI6jhuq6C0gqv+uZQDOcWM6h3LlOs1X5OIiNQ9XbdP1qtXL3r06MGMGTOq9nXo0IGhQ4cyderUk47/4osvuPXWW9m9ezdhYb9t6LfOg2fLLS7n3nfXsnLXERx2G3+9vjO392phdSwRETmFc7lmq4eQnNJzi7ZxIKeYmFA//nS1PkEUERFxB2VlZaxdu5aBAwdW2z9w4EBWrlx5yvssWLCAxMREnn/+eaKjo2nbti0PPfQQxcXFp32e0tJS8vLyqt3EcwX7eTNn7IXclBBDpcvgz/M38eyibbhcHvW5sohIg2PppNLinr7fdYR3ftgHmEPFApz6NREREXEHmZmZVFZWEhkZWW1/ZGQk6enpp7zP7t27Wb58Ob6+vsyfP5/MzEwmTJhAVlbWaecRmjp1Kk899VSN55f6y8fLzgs3daVFmD/Tknbw6pJdpGYX8Y+bu2kFWhGReko9hKSaorIK/vTRRgBuu7A5fVuHW5xIREREfslms1XbNgzjpH3HuFwubDYb7733HhdeeCGDBw9m2rRpzJkz57S9hCZPnkxubm7VLTU1tcZfg9Q/NpuN3w9ow7Th3fB22Ph8Yxoj3viRrMIyq6OJiMhvoIKQVDNn5V5SsoqICvZl8uAOVscRERGRE4SHh+NwOE7qDZSRkXFSr6FjoqKiiI6OJjg4uGpfhw4dMAyD/fv3n/I+TqeToKCgajeRY27sEcNb4y4k0NeLtfuyuXH6CvZkFlodS0REzpEKQlKlrMLFWyv3AvB/A9sR5OttbSARERGpxsfHh4SEBJKSkqrtT0pKok+fPqe8T9++fTl48CAFBQVV+3bs2IHdbicmJqZW80rD1adVOPMn9CEm1I+9R4q4cfoK1uzNsjqWiIicAxWEpMpnGw9yKK+UiEAn13VrZnUcEREROYVJkybxxhtvMHv2bLZu3cqDDz5ISkoK48ePB8zhXqNGjao6/vbbb6dx48aMHTuWLVu2sHTpUh5++GHGjRuHn5+fVS9DGoDWEYHMn9CXbjHBZBeVc/sbP/LZxoNWxxIRkbOkgpAA5twDbyzbA8DoPnH4eOlXQ0RExB3dcsstvPjii0yZMoXu3buzdOlSFi5cSGxsLABpaWmkpKRUHd+oUSOSkpLIyckhMTGRESNGMGTIEF5++WWrXoI0IE0Cncy9pzdXdoykrMLF/e+v59UluzAMrUAmIuLubIaH/bXOy8sjODiY3NxcjYc/wcpdmdz++o/4etv5/pEBhAb4WB1JRERE1203ofMgv6bSZfD051t4c8VeAEb0asFT13XCy6EPGUVE6tK5XLP1F1oAqnoH3ZzQXMUgERERETknDruNJ4Z04vFrO2KzwXs/pnDX22soKK2wOpqIiJyGCkLCzxkFLN6Wgc0GY/vGWR1HREREROqpcf3iee2OBHy97Xy3/TDDX/2e9NwSq2OJiMgpqCAkzF5h9g4a0D6Slk0aWZxGREREROqzgZ2aMu+e3oQ38mFLWh43TF/B1rQ8q2OJiMgvqCDk4bIKy/ho7X4A7uofb3EaEREREWkIujUPYf6EvrSOaERabgk3v/o9S3cctjqWiIicQAUhD/feD/sorXDRJTqYXvFhVscRcW+pqyBjm9UpRERE6oXmYf58NL4PF7UMo6C0grFzVjN3Vcqv31FEROqECkIerKS8kre+3weYvYNsNpvFiUTcVFEWfHwPzLoSpvcyv89Rg1ZEROTXBPt789a4C7nhgmgqXQaPfLyJF77chsvlUQsdi4i4JRWEPNiCDQfJLCilaZAvg7tEWR1HxD1t/wKm94aN84CjRdON8+BfCfDlo2axSERERE7L6eVg2vBu/H5AGwD+/e0uJs5LprSi0uJkIiKeTQUhD2UYBrOOLjU/pm8c3g79KohUU5wD8++FD26BgnRo3AbuTIK7v4W4/lBZBt+/Ai93hxUvQblWUBERETkdm83GpCvb8sJNXfGy21iw4SAj31hFdmGZ1dFERDyWqgAeavnPmWw/lI+/j4PbLmxhdRwR97IzyewVtOF9wAZ9HoDxy6B5T4juAaP/ByP+CxGdoCQXkh6HVxIh+QNw6dNOERGR07k5sTlvjbuQQKcXq/ZmMWzGSvYdKbQ6loiIR1JByEO9frR30PDE5gT7eVucRsRNlOTCp/fDezdB/kEIawXjvoCBT4O33/HjbDZoc6VZJLp+OgRFQ24qfDIeXrsEfv4aDM2NICIicip9W4fz33v7EB3ix+7MQm6YvpJ1KdlWxxIR8TgqCHmg7en5LN1xGJsNxvXVUvMiAPz8jdkraP07gA0uug/GL4cWF53+PnYHXDACHlgLVzwJzmA4tAneHQbvDIW0DXUUXkREpH5p1zSQ+RP60Dk6iKzCMm6b+QOLNqVZHUtExKOoIOSBZi83ewdd1bEpLRr7W5xGxGIlebDg9/DujZB3AELjYexCuPoZ8DnL/x/eftDvQfhDsllIsnvD7u/gtYvho7she19tvgIREZF6KSLIl3n39Oby9hGUVriY8P463li2G0O9bEVE6oQKQh7mcH4p85MPAOZS8yIebfd3MKMPrHvL3L7wd3DvCojt89sezz/MLCQ9sAa63Gzu2/Qfc34hrUgmIiJykgCnFzNHJjDyolgMA57+fCtPLNhMRaXL6mgiIg2el9UBpG6988M+yipcdG8eQkJsqNVxRKxRWmBOBL1mlrkdEgvX/xvi+9fM44fGwbA3oPd95vPsWWquSLbuHeg/CXr9rvqcRHL2Ksthx5eQ/D7sWgx+Ieb5C2lR/RYaC0Ex4OVjdWIREfkVXg47U67vRGxjf/62cCtvf7+PA9nFvHzbBQQ49XZFRKS22AwP65OZl5dHcHAwubm5BAUFWR2nTpWUV9Ln2cVkFZbxyu0XcG3XZlZHEql7e5bBpxMgJ8Xc7nkXXPEUOBvVzvMZhjk/UdLjkLHZ3BcUA5c/Cl1vMechkl+X/hMkvwcb/wNFmWd5JxsENTuhUPSLwlFwDDg0qb678+TrtjvReZC6smhTGhPnJVNa4aJzdBCzR/ckIsjX6lgiIvXGuVyzVXL3IPPXHyCrsIzoED+u7tTU6ji1x1UJK16EdW+DTyMIjIKgKAhsdvTr0VtQM/BvbK4YJQ1fWSF8/SSsmmluBzeH61+BlpfW7vPabNDmCmh1GWycB4ufhrz98Mm98P2/zWJU6wH6PTyVoizY9KFZCDpxgu5GkWYxrctN4Kowi3sn3rL3mV8ris15ofIOQMr3Jz++zW7+XQg9RQ+jkFhz9TiHLpMiInVpUJcoIoN9ufutNfx0II8bpq9k9pietGsaaHU0EZEGRz2EPITLZTDwxaX8nFHAX67pwF39W1odqXbkpMLH90DKyrM73uEDgU1PXSyqKiRFaXhPfbdvpVmAyd5rbieMgSv/Cr4W/A0oL4YfX4Nl06A019wXfwlcOQWada/7PO6msgJ2fQPr34Xti8BVbu63e0O7QXDBHdBqwK8XagwDCjOPFon2nlw0ykmBipIzP4bNYRaFThyGdmLRKLCZCkZ1wFOv2+5G50Hq2r4jhYx9czW7MwsJdHox444E+rUJtzqWiIjbO5drtgpCHuLb7RmMfXM1jZxefD/5cgJ9G+AwiZ8+gv89aL7J9mkEA/8KwS0g/yDkpVX/mp8OhYfP/rF9Q35RJDpVb6NwsGuedrdSVgSL/wo/zAAMc6jWdS+bPXKsVpQFy/5h9liqLDP3dbkZLv+LOQeRpzm83SwCbZwHBYeO74/qBt1HmP82/mE193wul/k3ICcFcvb94uvR27Hzcjo2BwRHHx2KFnvyHEaBURoSWAM89brtbnQexAo5RWXc8/ZaVu3Nwstu45kbuzA8sbnVsURE3JoKQmfgqQ2aEW/8wIqfj3BXv3j+cm1Hq+PUrNJ8WPgwbPjA3I5OhGGvQ9iv9IKqKIOC9FMXi07cV1F8djns3kd7G0WZX6sKSCd+bQo+Aef3euXspPwAn0yArF3mdo9RMPBp8A22NtcvZe8zh5Ft+o+57fCBnnfDxQ/VbAHEHRXnmIXc5PfgwNrj+/0bm0PCuo+App2tyeZymYWpqgLRL4tGqcd7L52O3cucp6iqUBT3ix5GTVUwOgueet12NzoPYpXSikoe/nAjCzYcBOD3l7fmwSvbYtNQaxGRU1JB6Aw8sUGz5WAeg19eht0GS/94GTGh/lZHqjmpq+Hju8yhQDY79H8ILvljzU0UaxhQknO0QHT0dqoCUkEGcJb/lZzBx3sX/XJo2rF9AU30RvG3Ki82Cyzf/xswzN5c170Mba60OtmZHUw+uiLZEnPbGQz9H4Re4xvWkEVXJez+ziwCbf0MKkvN/XYvaHMVdL8d2gx0/9XBXC6zoHzinEUn9jDKTTXnNzoTu/fRglFzCIiAgHCzp2HAsVuT49u+wR47z5QnXrfdkc6DWMnlMpiWtINXvv0ZgBsuiObZYV1weqmtJCLySyoInYEnNmj+7z8b+Gjdfq7pGsW/b+9hdZyaUVlhDrdZ8hwYleYEwTfOhNg+FuUpN3sTVCsWHSsgHTxeSCovPLvHszmO9zYKi4cO15lvkr21ysYZpa425wo6stPc7j4CrnrGXJq8PjAMc/6cpCfg0E/mvqBouOxR6HZr/S4SZv4MG96HDXPNSZ6PiegEF4yALsOhURPr8tU0V6X5//6XE10fKxrl7jf/dp0tu/dpCkaNza+//JkzqMEUkDzxuu2OdB7EHcxdlcKjn/xEpcvgopZhvHZHIsH+DXAaBBGR86CC0Bl4WoMmI6+Evs8tprzS4JP7+tK9eYjVkc5f9j5z4ujUH8ztzjfBNf9w/zf9hgGleUeHpB08uVh0rIBUcAgM18n3dwZDx+vMoTSxfTVf0YnKS+C7Z2Dlv8x/u0ZNzV5Bba+yOtlv46o0l1c/tiIZmIWTK5+C1lfUnzf6JXmw5RNY/97x/69gzsnVdbjZGyiqe/15PTWpssIsHh8rDhVmQlGmOa9R4RHza1Gmub+s4Nwf3+Fz+t5G1baPFpR8GrntefC067a70nkQd7F0x2EmvLeOgtIKWjUJYM7YC2ke1oB6v4uInCcVhM7A0xo0L3y5jX9/u4vE2FD+e69FvWdq0sYP4fNJZmHFJ9AsBHUd7rZvZH6TygoozDje2yh1lTnPyom9KoKiofMw87VHdm5Yr/9cHVgL8++FzO3mdtdbYdCz4Bdqba6aUF4Cq14ze8OVHFuR7OKjK5JdYG2203G5YO8ySH4fti6A8iJzv81uFrO63w7tBoOX09qc9Ul58QkFoxOLRb8sHh3dPtueiCdyOI/2NGp8huLRCT+vw7nQPO267a50HsSdbE3LY9yc1aTllhDeyIc3RvdsGB96iojUABWEzsCTGjRFZRX0eXYxOUXlvHpHD67uHGV1pN+uJNecOHrjPHM75kJziFhYvLW56orLBftWmBMPb/70+HLlAE06mIWhLjebc5F4iopS+O5ZWPGi2SsoIAKGvATtB1udrOadakWyzjfBgMfcZ0Wy7L2Q/IE5LCwn5fj+8Lbm0L2ut5hzZUntKyuqXiCq+j7z1D2Rznbi/BN5+R0vGJ1YLEq8s8b/LnvSddud6TyIu0nPLWHcnNVsScvD19vOS7dewFWdmlodS0TEcioInYEnNWje+WEfj33yEy3C/Pn2oUtx2OtpL5KUH82Jo3NSzF4Gl/zJnDza4WV1MmuUl8DOr8zi0I4vqy+N3aKPWRzqeH3DXqHq4HpzBbGMLeZ2l5th0PMN+zWDOVzy278dL4zaveHCu+Hih6157WWFsOVTszfQ3mXH9zuDofONZiEoJtGze7DVB2WFp+ltlAlFR6oXkwoPH58I/FTuWgwxCTUaz5Ou2+5M50HcUUFpBfe/v47vth/GZoPHrunIuH4e8mGhiMhpqCB0Bp7SoHG5DAZMW8KezEKeHNKRMX3r4cWxsgKWvgBLnzd7gIS0gBvfgBa9rE7mPopzzGE5G/8De5dTtdKZ3duchLrrzdD26oazSlVFmfn7sGyaOSGvfzhc+09zbiVPkrbBnHh697fmtjMI+j0IF91b++faMCDle3NeoC2fnDC/jQ1aXgoX3AHtr2k4v3NSnWGY5/yk3kZHt/s9WOOTg3vKddvd6TyIu6qodPH4gs28/6PZO3VMnzgeu7Zj/f0gVETkPKkgdAae0qBJ2nKIu99eQ6CvFz9MHkCAs571psnaY04cvX+Vud31Fhj8grn0spxa7gH46b/mPEuHNh3f7wwyVynrejPE9a+/K1WlbTB7BR1bfavTDTD47+aQFU/187EVyY6e76BouOzP0O22mj/POanmCmHJ70H2nuP7w1qa8wJ1vdWzhixKnfGU67a703kQd2YYBq8t3c2zi7YBcEWHSF6+rTv+PvWs/SsiUgNUEDoDT2nQ3PLa9/y4J4vfXdKSyYM6WB3n7BmGORzm84egLN8sZlwzzSxmyNk7tMUcUrbpv5Cbenx/YNTxyaibdq0fQ3kqy835c5a+AK4Kc5nta/5hFoTEnF9q09EVyY6d64iOcMVT0ObK8zvH5cWw9TNIfhd2L6GqB5pPI+g0FLrfAS0uqh+/R1Jvecp1293pPEh98NnGg0z6zwbKKlx0jg5i2vDutI0MtDqWiEidUkHoDDyhQbNpfy5DXlmOl93Gsj9dRlRwPRm6UZwDn/+f2csFoPlF5sTRobGWxqrXXC5zue+N/4HN86Ek5/jPwtuZhbYuN7vPxMS/lP4TfDIe0o/2gOlwnVkgrOEhKQ1CeYk56fSyvx9fkSyuv7kiWXSPs38cw4D9a8wi0E8fmyv6HRPX35wXqON1dbrKlHg2T7hu1wc6D1JfrNmbxd1vryG7qBxvh40Jl7ZmwmWtcHrV0x7SIiLnSAWhM/CEBs3Euev5JPkgQ7s348Vb3XRp6l/a9705RCw3BWwOuPQR6DfJcyeOrg0VZfBzklkc2vEFVJQc/1nzXkcno77BXCnIapXlsPxFWPIcuMrNJeQH/93s3aTeKGdWlAXLp8GPr52wItkwuPyxM6/+lJcGG+eaE0Rn7ji+P7iFOSSs+23uWziUBs0Trtv1gc6D1CdpucU89slPfL01A4A2EY14dlhXEmJDLU4mIlL7VBA6g4beoEnLLab/c99S4TL43/396BLj5nPuVJabb/qX/cOcODo0zpw4unlPq5M1bCV5sPV/5vC8PUs5Phm1F7S+wuw11G4w+PjXfbZDW+CTeyEt2dxud405cXRgZN1nqc9yUmDxsRXJDHOi8Z53mSuSHSv6VZTC9oXmBNG7vjH/D4K5pHjH6+GCERDbD+x2y16GSEO/btcXOg9S3xiGwWcb03hywWaOFJZhs8Ho3nE8fFW7+je3pojIOVBB6AwaeoNm6qKtvLZkN73iw5j3u95WxzmzrN3w0d1wYI253e12GPQc+Da88+LW8tLgp4/MeWjSNhzf79MIOgwxi0Pxl9R+b63KClj5Enz3rNmzxTfEnEi8y83qFXQ+0jbC10/ArsXmtjMIet9vrgy16cPqwwhb9DZ7A3Ucqv+H4jYa+nW7vtB5kPoqu7CMpz/fykfr9gMQHeLH0zd05rJ2ERYnExGpHSoInUFDbtAUllZw0dRvyC+p4PVRiVzZ0U17VBgGbPgAFj5sLl/sDIYh/zSHtYi1Dm83h5Rt+o/Zw+SYgAjocpNZnGl2Qc0XaDK2mb2CDq4zt9teDUNegsCmNfs8nmzXYkh6/Ph8TMcERUO3W825gRq3siabyBk05Ot2faLzIPXd0h2H+fP8TezPLgZgaPdmPHZtRxo3clqcTESkZqkgdAYNuUEzZ8UenvzfFuLDA/hm0iXY7W7Yq6I4Gz570JzgGKBFH3PiaC1X7V4MA1JXmcONNs+H4qzjP2vcGroMNyekDmt5fs/jqoTvXzGHNlWWmsXBQc+ZBQr1Cqp5LpfZK2jtHAiKMotALS+t+SXqRWpQQ75u1yc6D9IQFJVVMO2rHcxesQeXAWEBPjx+bUeu794Mm9odItJAqCB0Bg21QVPpMrjs79+RklXEX4d2ZuRFbrgy197l8PHvIG+/OXH0ZX+Gfg/qzai7qygze5ds+g9sWwgVxcd/FtPTLA51vhECws/tcTN3mr2C9q82t9sMNHsFBTWruewiUu811Ov2+Zo+fTovvPACaWlpdOrUiRdffJH+/fv/6v1WrFjBJZdcQufOnUlOTj7r59N5kIYkOTWHRz7ayLb0fAAubdeEp4d2JibUgrkTRURqmApCZ9BQGzRf/JTG+HfXEeLvzcpHLsffx40my6ssh++mwrJpgAGh8TBsFsQkWJ1MzlVpPmz9zCwO7f7u+CTENge0uhy63gLtB595SXJXJfwwAxb/1VzpzBkEV081e6vo0zkR+YWGet0+H/PmzWPkyJFMnz6dvn378tprr/HGG2+wZcsWWrRocdr75ebm0qNHD1q3bs2hQ4dUEBKPVlbhYubSXbz8zc+UVbrw93Hwx6vaMbJ3HA537GUvInKWVBA6g4baoLlpxkrW7Mvmvsta8fBV7a2Oc9yRXfDRXcfnhul+Bwx6FpyB1uaS85d/CDZ/bA4rO7j++H7vAGh/jbmMfcvLqk9GfWQXfDIBUn8wt1tdDtf9C4Jj6ja7iNQbDfW6fT569epFjx49mDFjRtW+Dh06MHToUKZOnXra+9166620adMGh8PBJ598ooKQCPBzRgGTP97I6r3ZAPRoEcJzw7rSJlJtVRGpn87lmq21hBuA9SnZrNmXjbfDxqjecVbHMRkGrHsHXu1vFoN8g+HmOTD03yoGNRSBkXDRvXDPd3D/GrjkT2bvr/JCswfRezfBtPaw8I+wf43ZK2hGX7MY5BMIQ16GOz5WMUhE5ByUlZWxdu1aBg4cWG3/wIEDWbly5Wnv9+abb7Jr1y6eeOKJs3qe0tJS8vLyqt1EGqLWEY2Yd09v/jq0M42cXqxLyWHwy8t48esdlFW4rI4nIlKr3GhckfxWs5bvAeC6btFEBvlanAYoyoLPJsKWT83tuP5ww6t649+Qhbcx54S6dDIcWGv2GvrpY3Np81Wvmbdj4i+B61+BkNMPaxARkVPLzMyksrKSyMjqK4lGRkaSnp5+yvvs3LmTRx55hGXLluHldXZNv6lTp/LUU0+dd16R+sButzHyolgGtI/gsU9+4pttGbz49U4Wbkrj2WFd6dEi1OqIIiK1Qj2E6rn92UUs+slsAN7ZL97iNMCepWYvkC2fgt0LBjwBoz5VMchT2GwQkwiDX4D/2wYj/mtOOu3tDz6N4Jpp5u+DikEiIufllysiGYZxylWSKisruf3223nqqado27btWT/+5MmTyc3Nrbqlpqaed2YRd9csxI83Rifyr9suoHGADzsOFTBsxkqeXLCZwtIKq+OJiNQ49RCq5+as2Euly6Bv68Z0bGbhmP6KMvj2b7DiJcCAsFYw7A2I7mFdJrGWwxvaXGneKkrNAqFWlBMROS/h4eE4HI6TegNlZGSc1GsIID8/nzVr1rB+/Xruv/9+AFwuF4Zh4OXlxVdffcXll19+0v2cTidOp7N2XoSIG7PZbAzp1ox+rcP56+db+HjdAeas3EvSlkP87YbOXNouwuqIIiI1Rj2E6rH8knLmrjY/sburX0vrgmTuhFlXwooXAQN6jILfLVUxSI7zcqoYJCJSA3x8fEhISCApKana/qSkJPr06XPS8UFBQWzatInk5OSq2/jx42nXrh3Jycn06tWrrqKL1CuhAT5MG96dt8ddSEyoHwdyihnz5momzl1PVmGZ1fFERGqEegjVY/NWp1JQWkHriEZc0rZJ3QcwDFj3FnwxGcqLwDcErnsZOl5f91lEREQ8xKRJkxg5ciSJiYn07t2bmTNnkpKSwvjx4wFzuNeBAwd4++23sdvtdO7cudr9IyIi8PX1PWm/iJzs4rZN+HLixfzjqx28uXIPnyQfZOnOTJ4Y0pHrujU75VBNEZH6QgWheqqi0sWbK/YC5txBdnsdX4yKsmDBA7DtM3M7/mIY+ioER9dtDhEREQ9zyy23cOTIEaZMmUJaWhqdO3dm4cKFxMbGApCWlkZKSorFKUUajgCnF48P6ciQblE88tEmth/K5w9zk/lk/QGevqEL0SF+VkcUEflNbIZhGFaHqEt5eXkEBweTm5tLUJCFc+6cp882HuT+99cTFuDDykcux9e7Dofj7P4O5o+H/DSwe8OAx6D3A2DXCEQREalZDeW6Xd/pPIiYyipcvLZkF/9a/DNllS4CfBz88er2jLwotu4/oBUROYVzuWbrHXw9ZBgGry8zl5q/46LYuisGVZTCV4/B20PNYlDjNnDX19D3DyoGiYiIiEiD5+Nl54EBbVj4h34kxoZSWFbJEws2c9OrK9l5KN/qeCIi50Tv4uuhdSnZbEjNwcfLzsiLYuvmSQ/vgDeugJUvAwYkjIXfLYFm3evm+UVERERE3ETriED+87ve/PX6TgT4OFiXksM1Ly/npa93UlbhsjqeiMhZUUGoHnrjaO+gG7pH0ySwlpeENQxYMxteuxjSN4JfGNzyHgx5EXwCave5RURERETclN1uY2TvOJImXcLl7SMoq3Txz693cO2/lrEuJdvqeCIiv0oFoXom5UgRX25OB+DO/vG1+2SFR2DuCPjsQagohpaXwr0rocO1tfu8IiIiIiL1RLMQP2aNTuTl2y6gcYAPOw4VMGzGSp7632YKSyusjicicloqCNUzs1fswWWYS2C2jQysvSfatRhm9Ibtn5sTRw98Gu6YD0FRtfecIiIiIiL1kM1m47puzfh60iXc2CMaw4A3V+xl4D+X8t32DKvjiYickgpC9UhucTn/WZMKwF39aqF3UFkR7F8Dix6Bd26AgkMQ3hbuXgx9tIqYiIiIiMiZhAb4MG14d94adyHRIX4cyClmzJureXBeMlmFZVbHExGpxsvqAHL25q5KoaisknaRgfRvE35+D1aYCWkbIH3T8duRnWCcMAle4p1mzyAf//N7LhERERERD3JJ2yZ89eDF/OOrHby5cg/z1x9gyY7DPDGkI9d1a4bNpiXqRcR6KgjVE+WVLuas3AuYcwed9UXE5YLsPScUfjaaX/PTTn18QBNo2hUuvAfaXV0z4UVEREREPEyA04vHh3RkSLcoHvloE9sP5fOHucl8sv4AT9/QhegQP6sjioiHU0Gonli4KY203BLCGzm5vnuzUx9UUQoZW6sXftJ/grL8Ux8f1gqadjFvUd3Mr40iQZ9YiIiIiIjUiAtahPK/B/rx6pJdvLL4Z77dfpiB05bwx6vbM/KiWOx2tb1FxBoqCNUDhmHw+rLdAIzqHYvTywHF2dWHe6VvgsPbwHWKlQwcTojoAFFdzd4/TbtAZCdw1uKk1CIiIiIiAoCPl53fD2jDoM5NeeTjTazdl80TCzazYMNBnhvWhdYRapeLSN1TQcjdGQbJmzYRlbaYgd4pjE8rhH9uhtyUUx/vG1K98NO0K4S3AYd3ncYWEREREZHq2kQG8uHvevPuj/t4btE21u7LZvBLy7nvstbce2krfLy0iIuI1B2bYRiG1SHqUl5eHsHBweTm5hIUFGR1nOoqyyFzB6RtrD7sqyTn1MeHtKhe+GnaBYJjNORLREQaDLe+bnsQnQeRmncwp5i/fPITi7eZy9K3jWzEs8O60qNFqMXJRKQ+O5drtnoIWaU035zfp6rws9Gc/6fy5OUoyw0HPxvRRHfoRVBcj6MFoM7gp4uFiIiIiEh91CzEj1mjE1mw4SBP/W8LOw4VMGzGSsb0ieOhge0IcOqtmojULv2VqW2GAfnpRws/JyzznrX71Mf7BB6f6LlpF6ZvC+DFjXb6tY9m9m096za7iIiIiIjUGpvNxvXdo+nfpglPf7aFj9cf4M0Ve/lq8yGeubELl7RtYnVEEWnAVBCqSa5KOLLreI+f9E3m8K+izFMfH9jshFW+jg75CokDuzl2OKeojJfnf0MZLu7qH193r0NEREREROpMWIAP027pznXdm/Ho/J84kFPM6NmrGNS5KXf1b0mPFiHYNC2EiNQwFYRqys9fw9w7oKL45J/Z7NC4zfGiz7E5fwLCz/iQ7/2YQkm5i45RQfRu2biWgouIiIiIiDu4tF0EXz14MX//ajtzVu5l0U/pLPopna4xwYztG8c1XZpp4mkRqTEqCNWU4BZmMcjb31zSvarw081c8t3H/5werqzCxVsr9wJwV/94fSIgIiIiIuIBApxePDGkE8MTm/Pmij18knyQjftzeXDeBp5ZuI0RvVowolcsTQKdVkcVkXrO8vLy9OnTiY+Px9fXl4SEBJYtW3bG40tLS3n00UeJjY3F6XTSqlUrZs+eXUdpz6BxK7hvNUzeD3d9Ddf+ExLHQUzCOReDAP634SAZ+aVEBjm5tmuzWggsIiIiIiLuqkNUEM/f1I3vH7mchwa2JTLIyeH8Ul78eid9n13MpHnJbNqfa3VMEanHLO0hNG/ePCZOnMj06dPp27cvr732GoMGDWLLli20aNHilPcZPnw4hw4dYtasWbRu3ZqMjAwqKirqOPkp2B3QpG2NPJRhGLyxfA8Ao/vEqVuoiIiIiIiHatzIyf2Xt+F3l7Ri0U/pzFmxh3UpOXy8/gAfrz9AYmwoY/rGcVWnpng79L5BRM6ezTAMw6on79WrFz169GDGjBlV+zp06MDQoUOZOnXqScd/8cUX3HrrrezevZuwsLDf9Jx5eXkEBweTm5tLUFDQb85em1b+nMntb/yIn7eD7ydfToi/j9WRRERELFEfrtueQOdBxL1sSM3hzRV7+HxTGuWV5tu5qGBf7rgoltsvbEFogN4/iHiqc7lmW1ZCLisrY+3atQwcOLDa/oEDB7Jy5cpT3mfBggUkJiby/PPPEx0dTdu2bXnooYcoLj7FRM5HlZaWkpeXV+3m7o71Dro5MUbFIBERERERqaZb8xBevPUCVvzpcn4/oA3hjXxIyy3hhS+3c9HUb3jko41sS3f/9z0iYi3LhoxlZmZSWVlJZGRktf2RkZGkp6ef8j67d+9m+fLl+Pr6Mn/+fDIzM5kwYQJZWVmnnUdo6tSpPPXUUzWev7b8nJHP4m0Z2Gwwrq+WmhcRERERkVOLCPJl0pVtue+yVny2IY03V+7hpwN5zF2dytzVqfRu2ZixfeMY0CESh12L1IhIdZavMvbL1bMMwzjtiloulwubzcZ7771HcHAwANOmTeOmm27i3//+N35+fifdZ/LkyUyaNKlqOy8vj+bNm9fgK6hZs5bvBeDKDpHEhQdYG0ZERERERNye08vBsIQYbuwRzdp92by5Yi9fbE7n+91H+H73EZqH+TG6dxw3JzYn2M/b6rgi4iYsKwiFh4fjcDhO6g2UkZFxUq+hY6KiooiOjq4qBoE555BhGOzfv582bdqcdB+n04nTWT+WZDxSUMrH6/YDcFf/lhanERERERGR+sRms5EYF0ZiXBgHc4p554d9fLAqhdSsYp7+fCvTknYwrEcMo/vE0TqikdVxRcRils0h5OPjQ0JCAklJSdX2JyUl0adPn1Pep2/fvhw8eJCCgoKqfTt27MButxMTE1OreevCez+mUFrhomtMMD3jQq2OIyIiIiIi9VSzED/+dHV7vn9kAM/e2IV2kYEUlVXyzg/7uGLaEkbNXsW32zNwuSxbY0hELGbpuoSTJk3ijTfeYPbs2WzdupUHH3yQlJQUxo8fD5jDvUaNGlV1/O23307jxo0ZO3YsW7ZsYenSpTz88MOMGzfulMPF6pOS8kre/n4vAHf2iz/tsDkREREREZGz5efj4NYLW/DFxP68f1cvruwYic0GS3ccZuybq7li2hLeWrmXgtIKq6OKSB2zdA6hW265hSNHjjBlyhTS0tLo3LkzCxcuJDY2FoC0tDRSUlKqjm/UqBFJSUk88MADJCYm0rhxY4YPH87TTz9t1UuoMQuSD5JZUEZUsC+Du0RZHUdERERERBoQm81Gn9bh9GkdTsqRIt7+fi/z1qSyO7OQJxZs5u9fbufmxOaM6RNHi8b+VscVkTpgMwzDo/oI5uXlERwcTG5uLkFBQVbHAcyJtK96cSk7DhXw58HtuefiVlZHEhERcQvueN32RDoPIg1TYWkFH63bz5wVe9mdWQiAzQYD2kcytm8cfVo11sgFkXrmXK7Zlq8yJrBsZyY7DhUQ4OPglp4trI4jIiIiIiIeIMDpxajecdzRK5alOw8zZ+Vevtt+mK+3HuLrrYdoG9mIMX3iueGCaPx8HFbHFZEapoKQG3h92W4AhvfUMpAiIiIiIlK37HYbl7aL4NJ2Eew6XMBbK/fy37X7zREM8zfx/JfbuLVnC0b1jqVZSP2eu1VEjrN0UmmB7en5LNuZid0G4/rGWx1HREREREQ8WKsmjZhyfWd++PMA/nJNB5qH+ZFTVM6rS3bR//lvmfDeWlbvzcLDZh4RaZDUQ8his5abvYOu7tyU5mGavE1ERERERKwX5OvNXf1bMrZvPIu3ZfDmij2s3HWEhZvSWbgpnU7NghjbN54h3aJwemk4mUh9pIKQhQ7nl/LJ+oMA3NmvpcVpREREREREqnPYbVzZMZIrO0ayPT2fOSv38PG6A2w+mMdDH27g2UVbuf3CFtxxUSwRQb5WxxWRc6AhYxZ654d9lFW6uKBFCAmxoVbHEREREREROa12TQOZemNXfpg8gD9d3Z5mwb5kFpTx8uKf6fPsYv4wdz3JqTlWxxSRs6SCkEVKyit594d9ANyl3kEiIiJyDqZPn058fDy+vr4kJCSwbNmy0x778ccfc+WVV9KkSROCgoLo3bs3X375ZR2mFZGGJjTAh3svbcXSP17G9BE96BkXSoXL4NPkgwz99wpumL6CT5MPUF7psjqqiJyBCkIW+XjdAbIKy4gJ9eOqTpFWxxEREZF6Yt68eUycOJFHH32U9evX079/fwYNGkRKSsopj1+6dClXXnklCxcuZO3atVx22WUMGTKE9evX13FyEWlovBx2BneJ4sPxffjsgX4M6xGDj8PO+pQc/jA3mX7PLeZf3+xkfUo2abnFVKhAJOJWbIaHTQ+fl5dHcHAwubm5BAUFWZLB5TK48p9L2HW4kMeu7cid/bS6mIiIyKm4w3Xb3fTq1YsePXowY8aMqn0dOnRg6NChTJ069aweo1OnTtxyyy08/vjjZ3W8zoOInK3D+aV8sCqFd37Yx+H80mo/s9sgItCXpsG+RAWbX5sGHdv2IyrYl4ggpyapFjkP53LN1qTSFliy4zC7DhcS6PRieGKM1XFERESknigrK2Pt2rU88sgj1fYPHDiQlStXntVjuFwu8vPzCQsLO+0xpaWllJYefyOXl5f32wKLiMdpEujk9wPaMP6SViz6KY3/rEllb2YR6XklVLoM0vNKSM8rITn19I/ROMCnWtEoKtivqnB0bL+/j97Kipwv/S+ywOvLzKXmb72wOYG+3hanERERkfoiMzOTyspKIiOrDzePjIwkPT39rB7jH//4B4WFhQwfPvy0x0ydOpWnnnrqvLKKiGfz8bJzffdoru8eDUCly+BIQSlpuSWk5ZaQnltMel4p6bnF5naeub+swsWRwjKOFJax+eDpi9FBvl5EBfsRGexLVNAveh0F+xIV5EeQnxc2m62uXrJIvaOCUB3bfDCXlbuO4LDbGNNXQ8VERETk3P3yDY5hGGf1pueDDz7gySef5NNPPyUiIuK0x02ePJlJkyZVbefl5dG8efPfHlhEPJ7DbiMiyJeIIF+6nebPiWEY5BSVHy0QHS0UHS0gHTpaMErLKaawrJK8kgrySvLZfij/tM/p5+04xdA0X5oeHZ7WNNiXMH8f7HYVjcQzqSBUx2Yt3wPA4C5RRIf4WZxGRERE6pPw8HAcDsdJvYEyMjJO6jX0S/PmzePOO+/kww8/5IorrjjjsU6nE6fTed55RUTOhc1mIzTAh9AAHzo2O/3cJ/kl5aSf0Kso/YReR8eKR9lF5RSXV7I7s5DdmYWnfSxvh43IoOqFouPb5tcmjZx4ObQekzQ8KgjVoUN5Jfxvw0EATSQtIiIi58zHx4eEhASSkpK44YYbqvYnJSVx/fXXn/Z+H3zwAePGjeODDz7gmmuuqYuoIiK1JtDXm0Bfb9pEBp72mJLyyuOForxi0nNPHp6WWVBKeaXB/uxi9mcXA9mnfKxjk2G3igigTUQg7ZoG0jayEW0iAwnSFCBSj6kgVIfe/n4v5ZUGPeNC6d48xOo4IiIiUg9NmjSJkSNHkpiYSO/evZk5cyYpKSmMHz8eMId7HThwgLfffhswi0GjRo3ipZde4qKLLqrqXeTn50dwcLBlr0NEpDb5ejuICw8gLjzgtMeUV7rIyD+hUFRVQDK/Tz/a26jihMmwV/x8pNpjNAv2pU2kWSRqE9GIdk0DaR3RSJNeS72g39I6UlRWwbs/pABwZ7+WFqcRERGR+uqWW27hyJEjTJkyhbS0NDp37szChQuJjY0FIC0tjZSUlKrjX3vtNSoqKrjvvvu47777qvaPHj2aOXPm1HV8ERG34e2wEx3id8apPI5Nhr0/p5ifDxWw45A5b9HOQwWk55VwMNe8LdlxuOo+Nhs0D/WnbWQj2kYGVt1aNgnA19tRFy9N5KzYDMMwrA5Rl/Ly8ggODiY3N5egoNOPS61p73y/l8c+3UxsY38W/9+lODRxmYiIyK+y6rot1ek8iIicLLeonJ0ZxwtE29Pz2ZmRT2ZB2SmPt9sgLjyAthGBtD067KxdZCBx4QF4a44iqSHncs1WD6E64HIZVZNJj+sbr2KQiIiIiIhIPRfs701iXBiJcWHV9h8pKGXH0d5Ex28F5BaXs/twIbsPF/LF5uOLA3g7bLQMb0SbowWiY0PQWoT5672j1CoVhOrAN9sy2HukiCBfL25KiLE6joiIiIiIiNSSxo2c9G7kpHerxlX7DMMgI7/UHHKWfrRH0aF8dh7Kp7Csku1Hh6J9RlrVfZxedlpHnDjszPw+OsQPuwpFUgNUEKoDry/bDcDtvWIJcOqfXERERERExJPYbOby9pFBvvRv06Rqv2EYHMgprioQHetR9HNGASXlLjYfzGPzwbxqj+Xv46BNZCBtj05i3SYykHaRgUQGObHZVCiSs6fqRC3buD+HVXuy8LLbGNMnzuo4IiIiIiIi4iZsNhsxof7EhPpzWfuIqv2VLoPUrKJqQ852HMpn1+ECisoq2ZCaw4bUnGqPFejrdXzI2bEJrZsGEt7IWcevSuoLFYRq2bG5g4Z0a0bTYF+L04iIiIiIiIi7c9htxIUHEBcewMBOTav2l1e62HekkB0nTGK9PT2fvUeKyC+pYM2+bNbsy672WGEBPlXDzdo1DSQhNpS2EYEadiYqCNWmgznFfL7RHAN6Z794i9OIiIiIiIhIfebtsNM6IpDWEYEM7hJVtb+0opLdhwtP6lGUklVEVmEZP+zO4ofdWVXHB/p6kRAbSmJsKIlxYXSLCcHPx2HFSxILqSBUi95auZcKl8FFLcPoHB1sdRwRERERERFpgJxeDjpEBdEhqvoy48VllfyccXzFs80H81ifkk1+SQXfbT/Md9sPA+Blt9E5OpiecaEkxIaRGBeqoWYeQAWhWlJQWsH7q1IAuLt/S4vTiIiIiIiIiKfx83HQJSaYLjHHOyhUVLrYlp7P6r1Z5hCzvVkcyislOTWH5NQcXl9mTnsSHx5AYmwoPePCSIgLpWV4gCatbmBUEKolH65JJb+kgpbhAVzWLuLX7yAiIiIiIiJSy7wcdjpHB9M5OpixfeMxDIP92cWs2ZfF6r3ZrN2bzfZD+ezJLGRPZiEfrt0PmHMRJcSG0jPOHGbWuVkwPl52i1+NnA8VhGpBpctg9gqzqjquX7wm6xIRERERERG3ZLPZaB7mT/Mwf264IAaA3KJy1qVkm72I9maTvD+HrMIykrYcImnLIQCcXna6NQ+p6kXUo0Uowf7eVr4UOUcqCNWCpC3ppGYVE+rvzbAeMVbHERERERERETlrwf7eXNY+gsvam6NdSisq+elAHmuP9SLal01WYRmr9mSxak8WsAubDdpGBJIYF2reYsOICfXTMDM3poJQLTg25vKOi2I1U7uIiIiIiIjUa04vBwmxoSTEhnLPxWAYBrszC1lztAfRmn3Z7MksZPuhfLYfyue9H835dJsG+ZIQF0rPo6uZtW8aiJdDw8zchQpCNWxdilkt9XHYGdk71uo4IiIiIiIiIjXKZrPRqkkjWjVpxC09WwBwOL+UtUcnqV6zL5ufDuSSnlfC5xvT+HxjGgABPg56HC0s9YwLo3vzEAKcKktYRf/yNWzWcrN30HXdmxER6GtxGhEREREREZHa1yTQydWdm3J156aAueT9hv05rNlrDjNbty+b/NIKlu3MZNnOTAAcdhsdo4KqCkSJcaFEBtXv99Eul0FhWQUFpRXkl5i3gtIKCkoqKCgtr7avqKyCqTd2tSyrCkI1KDWriEWbzMrnnf3iLU4jIiIiIiIiYg0/HwcXtWzMRS0bA+biSzsO5Vctdb9mbzYHcorZdCCXTQdymbNyLwDNw/zoGRtG4tECUesmjepkoabKY4WckhOLOeUnFHNOLPCUV20f+3nV96UV5/S8T17XCaeXNVPNqCBUg+as3IvLgP5twukQFWR1HBERERERERG34LDb6BAVRIeoIEZeZE6vcjCnmDX7sll7tBfRtvQ8UrOKSc06wMfrDwAQ7OdNQuzxiaq7xgTj6328gFLpMqoKMafqhVNQUkF+VdGmvOrY6sWccgrLKmv09Xo7bAT6etPI6WXefL0IdHoR6Gt+38jpTaCvF4ZRo097TlQQqiF5JeXMW50KqHeQiIiIiIiIyK9pFuLHdSF+XNetGQD5JeWsT8mpmodofUoOucXlLN6WweJtGQD4OOxEh/pRdLQ3T00Xcnwc9hOKNuYt0PfYV++q/dX2nbB97OcnFq3clQpCNeTrLYcoKK2gTUQjLmnbxOo4IiIiIiIiIvVKoK83F7dtwsVH31OXV7rYcjCvapjZ6r3ZZBaUsiez8KT7+njZf9ED53gvnEYn7A90Vu+h88tijlXDt6ygglANubFHDLGNAygpr8Rmq/3xjSIiIiIiIiINmbfDTrfmIXRrHsKd/eIxDIOUrCLSckto5PQi6GiPnQCnw6MKOTVFBaEalBAbanUEERERERERkQbJZrMR2ziA2MYBVkdpEOxWBxARERERERERkbqlgpCIiIiIiIiIiIdRQUhERERERERExMOoICQiIiIiIiIi4mFUEBIRERERERER8TAqCImIiIiIiIiIeBgVhEREREREREREPIwKQiIiIiIiIiIiHkYFIRERERERERERD6OCkIiIiIiIiIiIh1FBSERERERERETEw6ggJCIiIiIiIiLiYVQQEhERERERERHxMCoIiYiIiIiIiIh4GC+rA9Q1wzAAyMvLsziJiIiI/Jpj1+tj12+xhtpPIiIi9cO5tJ08riCUn58PQPPmzS1OIiIiImcrPz+f4OBgq2N4LLWfRERE6pezaTvZDA/7yM3lcnHw4EECAwOx2Ww1+th5eXk0b96c1NRUgoKCavSx5dzoXLgXnQ/3ovPhPnQufp1hGOTn59OsWTPsdo10t4raT55B58K96Hy4F50P96FzcWbn0nbyuB5CdrudmJiYWn2OoKAg/WK6CZ0L96Lz4V50PtyHzsWZqWeQ9dR+8iw6F+5F58O96Hy4D52L0zvbtpM+ahMRERERERER8TAqCImIiIiIiIiIeBgVhGqQ0+nkiSeewOl0Wh3F4+lcuBedD/ei8+E+dC5E9P/AnehcuBedD/ei8+E+dC5qjsdNKi0iIiIiIiIi4unUQ0hERERERERExMOoICQiIiIiIiIi4mFUEBIRERERERER8TAqCImIiIiIiIiIeBgVhGrI9OnTiY+Px9fXl4SEBJYtW2Z1JI80depUevbsSWBgIBEREQwdOpTt27dbHUswz43NZmPixIlWR/FYBw4c4I477qBx48b4+/vTvXt31q5da3Usj1RRUcFf/vIX4uPj8fPzo2XLlkyZMgWXy2V1NJE6pfaTe1D7yX2p/WQ9tZ/ch9pPNU8FoRowb948Jk6cyKOPPsr69evp378/gwYNIiUlxepoHmfJkiXcd999/PDDDyQlJVFRUcHAgQMpLCy0OppHW716NTNnzqRr165WR/FY2dnZ9O3bF29vbxYtWsSWLVv4xz/+QUhIiNXRPNJzzz3Hq6++yiuvvMLWrVt5/vnneeGFF/jXv/5ldTSROqP2k/tQ+8k9qf1kPbWf3IvaTzVPy87XgF69etGjRw9mzJhRta9Dhw4MHTqUqVOnWphMDh8+TEREBEuWLOHiiy+2Oo5HKigooEePHkyfPp2nn36a7t278+KLL1ody+M88sgjrFixQp++u4lrr72WyMhIZs2aVbVv2LBh+Pv7884771iYTKTuqP3kvtR+sp7aT+5B7Sf3ovZTzVMPofNUVlbG2rVrGThwYLX9AwcOZOXKlRalkmNyc3MBCAsLsziJ57rvvvu45ppruOKKK6yO4tEWLFhAYmIiN998MxEREVxwwQW8/vrrVsfyWP369eObb75hx44dAGzYsIHly5czePBgi5OJ1A21n9yb2k/WU/vJPaj95F7Ufqp5XlYHqO8yMzOprKwkMjKy2v7IyEjS09MtSiUAhmEwadIk+vXrR+fOna2O45Hmzp3LunXrWL16tdVRPN7u3buZMWMGkyZN4s9//jOrVq3i97//PU6nk1GjRlkdz+P86U9/Ijc3l/bt2+NwOKisrORvf/sbt912m9XRROqE2k/uS+0n66n95D7UfnIvaj/VPBWEaojNZqu2bRjGSfukbt1///1s3LiR5cuXWx3FI6WmpvKHP/yBr776Cl9fX6vjeDyXy0ViYiLPPPMMABdccAGbN29mxowZatBYYN68ebz77ru8//77dOrUieTkZCZOnEizZs0YPXq01fFE6ozaT+5H7Sdrqf3kXtR+ci9qP9U8FYTOU3h4OA6H46RPszIyMk761EvqzgMPPMCCBQtYunQpMTExVsfxSGvXriUjI4OEhISqfZWVlSxdupRXXnmF0tJSHA6HhQk9S1RUFB07dqy2r0OHDnz00UcWJfJsDz/8MI888gi33norAF26dGHfvn1MnTpVDRrxCGo/uSe1n6yn9pN7UfvJvaj9VPM0h9B58vHxISEhgaSkpGr7k5KS6NOnj0WpPJdhGNx///18/PHHLF68mPj4eKsjeawBAwawadMmkpOTq26JiYmMGDGC5ORkNWbqWN++fU9aQnjHjh3ExsZalMizFRUVYbdXvwQ7HA4tmyoeQ+0n96L2k/tQ+8m9qP3kXtR+qnnqIVQDJk2axMiRI0lMTKR3797MnDmTlJQUxo8fb3U0j3Pffffx/vvv8+mnnxIYGFj1yWNwcDB+fn4Wp/MsgYGBJ809EBAQQOPGjTUngQUefPBB+vTpwzPPPMPw4cNZtWoVM2fOZObMmVZH80hDhgzhb3/7Gy1atKBTp06sX7+eadOmMW7cOKujidQZtZ/ch9pP7kPtJ/ei9pN7Ufup5mnZ+Royffp0nn/+edLS0ujcuTP//Oc/tUynBU4378Cbb77JmDFj6jaMnOTSSy/VsqkW+uyzz5g8eTI7d+4kPj6eSZMmcffdd1sdyyPl5+fz2GOPMX/+fDIyMmjWrBm33XYbjz/+OD4+PlbHE6kzaj+5B7Wf3JvaT9ZS+8l9qP1U81QQEhERERERERHxMJpDSERERERERETEw6ggJCIiIiIiIiLiYVQQEhERERERERHxMCoIiYiIiIiIiIh4GBWEREREREREREQ8jApCIiIiIiIiIiIeRgUhEREREREREREPo4KQiIiIiIiIiIiHUUFIRDyOzWbjk08+sTqGiIiISL2gtpNIw6SCkIjUqTFjxmCz2U66XX311VZHExEREXE7ajuJSG3xsjqAiHieq6++mjfffLPaPqfTaVEaEREREfemtpOI1Ab1EBKROud0OmnatGm1W2hoKGB2SZ4xYwaDBg3Cz8+P+Ph4Pvzww2r337RpE5dffjl+fn40btyYe+65h4KCgmrHzJ49m06dOuF0OomKiuL++++v9vPMzExuuOEG/P39adOmDQsWLKj6WXZ2NiNGjKBJkyb4+fnRpk2bkxphIiIiInVFbScRqQ0qCImI23nssccYNmwYGzZs4I477uC2225j69atABQVFXH11VcTGhrK6tWr+fDDD/n666+rNVpmzJjBfffdxz333MOmTZtYsGABrVu3rvYcTz31FMOHD2fjxo0MHjyYESNGkJWVVfX8W7ZsYdGiRWzdupUZM2YQHh5ed/8AIiIiIudAbScR+U0MEZE6NHr0aMPhcBgBAQHVblOmTDEMwzAAY/z48dXu06tXL+Pee+81DMMwZs6caYSGhhoFBQVVP//8888Nu91upKenG4ZhGM2aNTMeffTR02YAjL/85S9V2wUFBYbNZjMWLVpkGIZhDBkyxBg7dmzNvGARERGR86C2k4jUFs0hJCJ17rLLLmPGjBnV9oWFhVV937t372o/6927N8nJyQBs3bqVbt26ERAQUPXzvn374nK52L59OzabjYMHDzJgwIAzZujatWvV9wEBAQQGBpKRkQHAvffey7Bhw1i3bh0DBw5k6NCh9OnT5ze9VhEREZHzpbaTiNQGFYREpM4FBASc1A3519hsNgAMw6j6/lTH+Pn5ndXjeXt7n3Rfl8sFwKBBg9i3bx+ff/45X3/9NQMGDOC+++7j73//+zllFhEREakJajuJSG3QHEIi4nZ++OGHk7bbt28PQMeOHUlOTqawsLDq5ytWrMBut9O2bVsCAwOJi4vjm2++Oa8MTZo0YcyYMbz77ru8+OKLzJw587weT0RERKS2qO0kIr+FegiJSJ0rLS0lPT292j4vL6+qyQc//PBDEhMT6devH++99x6rVq1i1qxZAIwYMYInnniC0aNH8+STT3L48GEeeOABRo4cSWRkJABPPvkk48ePJyIigkGDBpGfn8+KFSt44IEHzirf448/TkJCAp06daK0tJTPPvuMDh061OC/gIiIiMjZU9tJRGqDCkIiUue++OILoqKiqu1r164d27ZtA8xVLObOncuECRNo2rQp7733Hh07dgTA39+fL7/8kj/84Q/07NkTf39/hg0bxrRp06oea/To0ZSUlPDPf/6Thx56iPDwcG666aazzufj48PkyZPZu3cvfn5+9O/fn7lz59bAKxcRERE5d2o7iUhtsBmGYVgdQkTkGJvNxvz58xk6dKjVUURERETcntpOIvJbaQ4hEREREREREREPo4KQiIiIiIiIiIiH0ZAxEREREREREREPox5CIiIiIiIiIiIeRgUhEREREREREREPo4KQiIiIiIiIiIiHUUFIRERERERERMTDqCAkIiIiIiIiIuJhVBASEREREREREfEwKgiJiIiIiIiIiHgYFYRERERERERERDzM/wN+F48cWxfeDAAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot accuracy and loss for the Custom CNN model\n",
+ "plot_accuracy_and_loss(history_vgg16, 'VGG16 Transfer Learning Model')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 122ms/step\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get predictions for the test set\n",
+ "y_pred = combined_model.predict(X_test)\n",
+ "y_pred_classes = np.argmax(y_pred, axis=1)\n",
+ "y_true = np.argmax(y_test, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Classification Report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " airplane 0.72 0.69 0.70 1000\n",
+ " automobile 0.83 0.68 0.75 1000\n",
+ " bird 0.51 0.67 0.58 1000\n",
+ " cat 0.49 0.42 0.45 1000\n",
+ " deer 0.52 0.71 0.60 1000\n",
+ " dog 0.61 0.52 0.56 1000\n",
+ " frog 0.69 0.69 0.69 1000\n",
+ " horse 0.80 0.63 0.71 1000\n",
+ " ship 0.73 0.80 0.76 1000\n",
+ " truck 0.75 0.73 0.74 1000\n",
+ "\n",
+ " accuracy 0.65 10000\n",
+ " macro avg 0.67 0.65 0.65 10000\n",
+ "weighted avg 0.67 0.65 0.65 10000\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Classification report (Precision, Recall, F1-Score)\n",
+ "print(\"Classification Report:\")\n",
+ "print(classification_report(y_true, y_pred_classes, target_names=class_names))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Compute the confusion matrix\n",
+ "conf_matrix = confusion_matrix(y_true, y_pred_classes)\n",
+ "\n",
+ "# Plot the confusion matrix\n",
+ "plt.figure(figsize=(10, 8))\n",
+ "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
+ "plt.xlabel('Predicted Labels')\n",
+ "plt.ylabel('True Labels')\n",
+ "plt.title('Confusion Matrix for CIFAR-10 after Fine-Tuning VGG16')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Save Model as pickle file\n",
+ "import pickle\n",
+ "with open('VGG16_Charlie_Dani.pkl', 'wb') as f:\n",
+ " pickle.dump(combined_model, f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Tensorflow/main_tensorflow.py b/Tensorflow/main_tensorflow.py
new file mode 100644
index 00000000..dc1b7cbf
--- /dev/null
+++ b/Tensorflow/main_tensorflow.py
@@ -0,0 +1,148 @@
+# Import all the necessary libraries
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.datasets import cifar10
+from tensorflow.keras import layers, models
+from tensorflow.keras.utils import to_categorical
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.layers import GlobalAveragePooling2D
+from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras.applications import VGG16
+from sklearn.metrics import confusion_matrix, classification_report
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+# Load the CIFAR-10 dataset and divide it into training and testing sets
+(X_train, y_train), (X_test, y_test) = cifar10.load_data()
+
+# Check the shape of the datasets
+print(f"Training data shape: {X_train.shape}, Training labels shape: {y_train.shape}")
+print(f"Test data shape: {X_test.shape}, Test labels shape: {y_test.shape}")
+
+# Normalize the images scaling pixel values to be between 0 and 1
+X_train = X_train.astype('float32') / 255.0
+X_test = X_test.astype('float32') / 255.0
+
+# Convert class labels to one-hot encoding
+y_train = to_categorical(y_train, 10)
+y_test = to_categorical(y_test, 10)
+
+# Display a few random images from the training set
+plt.figure(figsize=(10,10))
+for i in range(9):
+ plt.subplot(3, 3, i+1)
+ plt.imshow(X_train[i])
+ plt.title(f"Class: {y_train[i].argmax()}")
+ plt.axis('off')
+plt.show()
+
+# Build a Convolutional Neural Network (CNN) suitable for image classification.
+
+# Model / data parameters
+num_classes = 10
+input_shape = (32, 32, 3)
+
+# Build the CNN with convolutional blocks and poolings (flatten, dropout and dense at the end to fully connect layers)
+model = keras.Sequential(
+ [
+ keras.Input(shape=input_shape),
+ layers.Conv2D(32, kernel_size=(3, 3), activation="relu", padding='same'),
+ layers.BatchNormalization(), # Adding batch normalization after each convolutional block can help stabilize and faster the training.
+ layers.Conv2D(32, kernel_size=(3, 3), activation="relu", padding='same'),
+ layers.BatchNormalization(),
+ layers.MaxPooling2D(pool_size=(2, 2)),
+ layers.Dropout(0.25), # Add dropout to reduce overfitting
+
+ layers.Conv2D(64, kernel_size=(3, 3), activation="relu", padding='same'),
+ layers.BatchNormalization(),
+ layers.Conv2D(64, kernel_size=(3, 3), activation="relu", padding='same'),
+ layers.BatchNormalization(),
+ layers.MaxPooling2D(pool_size=(2, 2)),
+ layers.Dropout(0.25),
+
+ layers.Conv2D(128, kernel_size=(3, 3), activation="relu", padding='same'),
+ layers.BatchNormalization(),
+ layers.Conv2D(128, kernel_size=(3, 3), activation="relu", padding='same'),
+ layers.BatchNormalization(),
+ layers.MaxPooling2D(pool_size=(2, 2)),
+ layers.Dropout(0.3),
+
+ layers.Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same'),
+ layers.BatchNormalization(),
+ layers.Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same'),
+ layers.BatchNormalization(),
+ layers.MaxPooling2D(pool_size=(2, 2)),
+ layers.Dropout(0.4),
+
+ layers.Flatten(),
+ layers.Dense(256, activation="relu"), # Add a fully connected layer before the output
+ layers.Dropout(0.5), # Increase dropout for the fully connected layer
+ layers.Dense(num_classes, activation="softmax"),
+ ]
+)
+
+model.summary()
+
+# Compile the model
+model.compile(loss="categorical_crossentropy", optimizer=Adam(learning_rate=0.0003), metrics=["accuracy"])
+
+# Train the model
+history_cnn = model.fit(X_train[:8000], y_train[:8000], batch_size=32, epochs=60, validation_split=0.1)
+
+# Function to plot the training and validation accuracy and loss
+def plot_accuracy_and_loss(history, model_name):
+ # Plot accuracy
+ plt.figure(figsize=(14, 5))
+
+ # Plot Accuracy
+ plt.subplot(1, 2, 1)
+ plt.plot(history.history['accuracy'], label='Train Accuracy')
+ plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
+ plt.title(f'{model_name} - Accuracy')
+ plt.xlabel('Epochs')
+ plt.ylabel('Accuracy')
+ plt.legend()
+
+ # Plot Loss
+ plt.subplot(1, 2, 2)
+ plt.plot(history.history['loss'], label='Train Loss')
+ plt.plot(history.history['val_loss'], label='Validation Loss')
+ plt.title(f'{model_name} - Loss')
+ plt.xlabel('Epochs')
+ plt.ylabel('Loss')
+ plt.legend()
+
+ plt.show()
+
+# Plot accuracy and loss for the Custom CNN model
+plot_accuracy_and_loss(history_cnn, 'CNN Model')
+
+# List of CIFAR-10 class names
+class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+
+# Compute and report metrics
+score = model.evaluate(X_test, y_test, verbose=3)
+print("Test loss:", score[0])
+print("Test accuracy:", score[1])
+
+# Get predictions for the test set
+y_pred = model.predict(X_test)
+y_pred_classes = np.argmax(y_pred, axis=1) # Convert predicted probabilities to class labels
+y_true = np.argmax(y_test, axis=1) # Convert one-hot encoded labels to class labels
+
+# Compute the confusion matrix
+conf_matrix = confusion_matrix(y_true, y_pred_classes)
+
+# Visualize the confusion matrix to understand model performance across different classes.
+plt.figure(figsize=(10, 8))
+sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)
+plt.xlabel('Predicted Labels')
+plt.ylabel('True Labels')
+plt.title('Confusion Matrix for CIFAR-10')
+plt.show()
+
+# Save Model as pickle file
+import pickle
+with open('CNN_Charlie_Dani.pkl', 'wb') as f:
+ pickle.dump(model, f)
\ No newline at end of file
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+
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+
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import necessary libraries\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import sklearn\n",
+ "import cv2\n",
+ "import pickle\n",
+ "from tensorflow import keras\n",
+ "from keras.datasets import cifar10\n",
+ "from keras.utils import to_categorical\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Conv2D, MaxPooling2D, Flatten, GlobalAveragePooling2D, Dense, Dropout, RandomFlip, RandomZoom, RandomRotation, Rescaling, BatchNormalization\n",
+ "from keras.optimizers import Adam, SGD\n",
+ "from keras.applications.xception import preprocess_input\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(50000, 96, 96, 3) (50000, 10) (10000, 96, 96, 3) (10000, 10)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load CIFAR10 model using 'unpickle' function\n",
+ "def unpickle(file):\n",
+ " import pickle\n",
+ " with open(file, 'rb') as fo:\n",
+ " dict = pickle.load(fo, encoding='bytes')\n",
+ " return dict\n",
+ "\n",
+ "#open all the batches\n",
+ "batch_files = [f'./cifar-10-batches-py/data_batch_{i}' for i in range(1, 6)]\n",
+ "\n",
+ "# open the meta batch\n",
+ "meta_data = unpickle('./cifar-10-batches-py/batches.meta')\n",
+ "label_names = meta_data[b'label_names']\n",
+ "# Decode byte strings to regular strings\n",
+ "label_names = [label.decode('utf-8') for label in label_names]\n",
+ "\n",
+ "# Initialize empty lists to store the data and labels\n",
+ "data_list = []\n",
+ "labels_list = []\n",
+ "\n",
+ "# Loop over each batch file and load the data\n",
+ "for file in batch_files:\n",
+ " batch = unpickle(file)\n",
+ " data_list.append(batch[b'data']) # Append image data\n",
+ " labels_list.append(batch[b'labels']) # Append labels\n",
+ "\n",
+ "# Concatenate all data and labels into a single dataset\n",
+ "X_train = np.concatenate(data_list)\n",
+ "y_train = np.concatenate(labels_list)\n",
+ "# load the first batch for smaller training sample\n",
+ "#batch1 = unpickle('./cifar-10-batches-py/data_batch_1')\n",
+ "#X_train = batch1[b'data'][:3000]\n",
+ "#y_train = batch1[b'labels'][:3000]\n",
+ "\n",
+ "# Optionally, load the test batch\n",
+ "test_batch = unpickle('./cifar-10-batches-py/test_batch')\n",
+ "X_test = test_batch[b'data']\n",
+ "y_test = np.array(test_batch[b'labels'])\n",
+ "\n",
+ "# Reshape the training and test data to 32x32x3 (for images)\n",
+ "X_train = X_train.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)\n",
+ "X_test = X_test.reshape(-1, 3, 32, 32).transpose(0, 2, 3, 1)\n",
+ "\n",
+ "\n",
+ "# Resize the images to 299x299\n",
+ "X_train_resized = np.array([cv2.resize(img, (96, 96)) for img in X_train])\n",
+ "X_test_resized = np.array([cv2.resize(img, (96, 96)) for img in X_test])\n",
+ "\n",
+ "\n",
+ "# Normalize the images\n",
+ "X_train = X_train_resized.astype('float16') /255\n",
+ "X_test = X_test_resized.astype('float16') /255\n",
+ "\n",
+ "# One-hot encode the labels\n",
+ "y_train = to_categorical(y_train)\n",
+ "y_test = to_categorical(y_test)\n",
+ "\n",
+ "#train_generator = train_datagen.flow(X_train_resized, y_train, batch_size=32)\n",
+ "#test_generator = test_datagen.flow(X_test_resized, y_test, batch_size=32)\n",
+ "\n",
+ "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\danis\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+ " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m590,336\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n",
+ "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m262,656\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m5,130\u001b[0m │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/100\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "KeyboardInterrupt\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from tensorflow.keras import layers, models\n",
+ "from tensorflow.keras.optimizers import Adam\n",
+ "\n",
+ "# Initialize the Sequential model\n",
+ "model = models.Sequential()\n",
+ "\n",
+ "# First Convolutional Block\n",
+ "model.add(layers.Conv2D(64, (3, 3), padding='same', activation='relu', input_shape=(32, 32, 3)))\n",
+ "model.add(layers.BatchNormalization()) # Batch Normalization to stabilize training\n",
+ "model.add(layers.Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
+ "model.add(layers.BatchNormalization())\n",
+ "model.add(layers.MaxPooling2D((2, 2))) # Downsample the feature maps\n",
+ "model.add(layers.Dropout(0.1)) # Dropout to prevent overfitting\n",
+ "\n",
+ "# Second Convolutional Block\n",
+ "model.add(layers.Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
+ "model.add(layers.BatchNormalization()) # Batch Normalization to stabilize training\n",
+ "\n",
+ "model.add(layers.MaxPooling2D((2, 2))) # Downsample the feature maps\n",
+ "model.add(layers.Dropout(0.2)) # Dropout for further regularization\n",
+ "\n",
+ "# Third Convolutional Block\n",
+ "model.add(layers.Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
+ "model.add(layers.BatchNormalization()) # Batch Normalization to stabilize training\n",
+ "\n",
+ "model.add(layers.MaxPooling2D((2, 2))) # Downsample the feature maps\n",
+ "model.add(layers.Dropout(0.2)) # Dropout for further regularization\n",
+ "\n",
+ "# Fourth Convolutional Block\n",
+ "model.add(layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
+ "model.add(layers.BatchNormalization()) # Batch Normalization to stabilize training\n",
+ "\n",
+ "model.add(layers.MaxPooling2D((2, 2))) # Downsample the feature maps\n",
+ "model.add(layers.Dropout(0.3)) \n",
+ "\n",
+ "# Fifth Convolutional Block\n",
+ "model.add(layers.Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
+ "model.add(layers.BatchNormalization()) # Batch Normalization to stabilize training\n",
+ "\n",
+ "model.add(layers.MaxPooling2D((2, 2))) # Downsample the feature maps\n",
+ "model.add(layers.Dropout(0.4)) \n",
+ "\n",
+ "\n",
+ "# Flatten the output and add Fully Connected layers (Dense layers)\n",
+ "model.add(layers.Flatten())\n",
+ "model.add(layers.Dense(512, activation='relu'))\n",
+ "model.add(layers.Dropout(0.2)) # Dropout to prevent overfitting\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Output Layer\n",
+ "model.add(layers.Dense(10, activation='softmax'))\n",
+ "\n",
+ "\n",
+ "# Summarize the model architecture\n",
+ "model.summary()\n",
+ "\n",
+ "# Define the epochs, batch size and number of classes\n",
+ "num_classes = 10\n",
+ "epochs = 100\n",
+ "batch_size = 64\n",
+ "\n",
+ "# Optimizers\n",
+ "Adam = Adam(learning_rate=0.00003)\n",
+ "SGD = SGD(learning_rate=0.00001, momentum=0.9, nesterov=True)\n",
+ "\n",
+ "# Other parameters\n",
+ "early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)\n",
+ "\n",
+ "reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=1e-6)\n",
+ "\n",
+ "# Compile the model\n",
+ "model.compile(optimizer= SGD,\n",
+ " loss='categorical_crossentropy',\n",
+ " metrics=['accuracy'])\n",
+ "\n",
+ "history = model.fit(X_train, y_train, \n",
+ " batch_size=batch_size, \n",
+ " epochs=epochs, \n",
+ " validation_split=0.2,\n",
+ " shuffle=True,\n",
+ " callbacks= [early_stopping, reduce_lr]\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load our model from pickle document\n",
+ "with open('./CNN__Charlie_Dani.pkl', 'rb') as f:\n",
+ " our_model = pickle.load(f) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tensorflow.keras import layers, models\n",
+ "from tensorflow.keras.optimizers import Adam, SGD\n",
+ "from tensorflow.keras.applications import Xception\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Load the pre-trained Xception model without the top layers\n",
+ "base_model = Xception(weights=\"imagenet\", include_top=False, input_shape=(96, 96, 3))\n",
+ "\n",
+ "# Your custom model architecture for the top layers\n",
+ "def build_custom_top_layers():\n",
+ " model = models.Sequential()\n",
+ " \n",
+ " # First Convolutional Block\n",
+ " model.add(layers.Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.MaxPooling2D((2, 2)))\n",
+ " model.add(layers.Dropout(0.25))\n",
+ "\n",
+ " # Second Convolutional Block\n",
+ " model.add(layers.Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.MaxPooling2D((2, 2)))\n",
+ " model.add(layers.Dropout(0.25))\n",
+ "\n",
+ " # Third Convolutional Block\n",
+ " model.add(layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.MaxPooling2D((2, 2)))\n",
+ " model.add(layers.Dropout(0.3))\n",
+ "\n",
+ " # Fourth Convolutional Block\n",
+ " model.add(layers.Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
+ " model.add(layers.BatchNormalization())\n",
+ " model.add(layers.MaxPooling2D((2, 2)))\n",
+ " model.add(layers.Dropout(0.4))\n",
+ "\n",
+ " # Flatten the output and add Fully Connected layers\n",
+ " model.add(layers.Flatten())\n",
+ " model.add(layers.Dense(256, activation='relu'))\n",
+ " model.add(layers.Dense(128, activation='relu'))\n",
+ " model.add(layers.Dropout(0.5))\n",
+ " \n",
+ " # Output Layer\n",
+ " model.add(layers.Dense(10, activation='softmax'))\n",
+ " \n",
+ " return model\n",
+ "\n",
+ "# Define the epochs, batch size and number of classes\n",
+ "num_classes = 10\n",
+ "epochs = 50\n",
+ "batch_size = 64\n",
+ "\n",
+ "# Optimizers\n",
+ "Adam = Adam(learning_rate=0.0003)\n",
+ "SGD = SGD(learning_rate=0.003, momentum=0.9, nesterov=True)\n",
+ "\n",
+ "# Other parameters\n",
+ "early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/5\n",
+ "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m390s\u001b[0m 306ms/step - accuracy: 0.5940 - loss: 5.8813 - val_accuracy: 0.6732 - val_loss: 6.3842\n",
+ "Epoch 2/5\n",
+ "\u001b[1m1250/1250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m301s\u001b[0m 241ms/step - accuracy: 0.6568 - loss: 6.9410 - val_accuracy: 0.6840 - val_loss: 6.5202\n",
+ "Epoch 3/5\n",
+ "\u001b[1m 373/1250\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3:00\u001b[0m 206ms/step - accuracy: 0.6730 - loss: 6.9909"
+ ]
+ },
+ {
+ "ename": "AbortedError",
+ "evalue": "Graph execution error:\n\nDetected at node StatefulPartitionedCall/functional_27_1/block10_sepconv3_1/separable_conv2d defined at (most recent call last):\n\nOperation received an exception:Status: 1, message: could not create a memory object, in file tensorflow/core/kernels/mkl/mkl_conv_ops.cc:1112\n\t [[{{node StatefulPartitionedCall/functional_27_1/block10_sepconv3_1/separable_conv2d}}]] [Op:__inference_one_step_on_iterator_19138]",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mAbortedError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[7], line 23\u001b[0m\n\u001b[0;32m 21\u001b[0m optimizer_2 \u001b[38;5;241m=\u001b[39m keras\u001b[38;5;241m.\u001b[39moptimizers\u001b[38;5;241m.\u001b[39mSGD(learning_rate\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, momentum\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.9\u001b[39m, nesterov\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 22\u001b[0m model_ImagNet\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39moptimizer_2, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcategorical_crossentropy\u001b[39m\u001b[38;5;124m'\u001b[39m, metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m---> 23\u001b[0m history \u001b[38;5;241m=\u001b[39m model_ImagNet\u001b[38;5;241m.\u001b[39mfit(X_train, y_train, epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m, validation_split\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m)\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m 120\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m 121\u001b[0m \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 124\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 52\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m 54\u001b[0m inputs, attrs, num_outputs)\n\u001b[0;32m 55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 56\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+ "\u001b[1;31mAbortedError\u001b[0m: Graph execution error:\n\nDetected at node StatefulPartitionedCall/functional_27_1/block10_sepconv3_1/separable_conv2d defined at (most recent call last):\n\nOperation received an exception:Status: 1, message: could not create a memory object, in file tensorflow/core/kernels/mkl/mkl_conv_ops.cc:1112\n\t [[{{node StatefulPartitionedCall/functional_27_1/block10_sepconv3_1/separable_conv2d}}]] [Op:__inference_one_step_on_iterator_19138]"
+ ]
+ }
+ ],
+ "source": [
+ "from tensorflow.keras.models import Model\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "\n",
+ "# Top layer consists of average pooling and output layer. So we are removing the top layer\n",
+ "base_model = Xception(weights=\"imagenet\", include_top=False, input_shape=(96, 96, 3))\n",
+ "# Freeze the base model layers\n",
+ "for layer in base_model.layers:\n",
+ " layer.trainable = False\n",
+ "\n",
+ "\n",
+ "\n",
+ "x = base_model.output\n",
+ "x = GlobalAveragePooling2D()(x)\n",
+ "x = Dropout(0.3)(x)\n",
+ "output = Dense(10, activation=\"softmax\")(x)\n",
+ "\n",
+ "# Pass the output from Xception to 'our_model'\n",
+ "model_ImagNet = Model(inputs=base_model.input, outputs=output)\n",
+ "\n",
+ "# Recompile the model\n",
+ "optimizer_2 = keras.optimizers.SGD(learning_rate=0.2, momentum=0.9, nesterov=True)\n",
+ "model_ImagNet.compile(optimizer=optimizer_2, loss='categorical_crossentropy', metrics=['accuracy'])\n",
+ "history = model_ImagNet.fit(X_train, y_train, epochs=5, batch_size=32, validation_split=0.2)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n",
+ "\u001b[1m 263/2500\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m16:49\u001b[0m 451ms/step - accuracy: 0.1655 - loss: 2.2079"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[76], line 17\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcallbacks\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ModelCheckpoint\n\u001b[0;32m 8\u001b[0m checkpoint \u001b[38;5;241m=\u001b[39m ModelCheckpoint(\n\u001b[0;32m 9\u001b[0m filepath\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_epoch_\u001b[39m\u001b[38;5;132;01m{epoch:02d}\u001b[39;00m\u001b[38;5;124m_val_acc_\u001b[39m\u001b[38;5;132;01m{val_accuracy:.4f}\u001b[39;00m\u001b[38;5;124m.keras\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;66;03m# Path to save the model file\u001b[39;00m\n\u001b[0;32m 10\u001b[0m monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_accuracy\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;66;03m# Metric to monitor (you can also use 'val_accuracy' or other metrics)\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 14\u001b[0m verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m \u001b[38;5;66;03m# Display info when saving\u001b[39;00m\n\u001b[0;32m 15\u001b[0m )\n\u001b[1;32m---> 17\u001b[0m history \u001b[38;5;241m=\u001b[39m model_ImagNet\u001b[38;5;241m.\u001b[39mfit(X_train, y_train, epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m16\u001b[39m, validation_split\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, callbacks\u001b[38;5;241m=\u001b[39m[checkpoint])\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m confusion_matrix\n\u001b[0;32m 20\u001b[0m gt \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(y_test, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:320\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[0;32m 318\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[0;32m 319\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m--> 320\u001b[0m logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_function(iterator)\n\u001b[0;32m 321\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n\u001b[0;32m 322\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 833\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m 836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m 876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m 877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m tracing_compilation\u001b[38;5;241m.\u001b[39mcall_function(\n\u001b[0;32m 879\u001b[0m args, kwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config\n\u001b[0;32m 880\u001b[0m )\n\u001b[0;32m 881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[0;32m 882\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 883\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m 137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m function\u001b[38;5;241m.\u001b[39m_call_flat( \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m 140\u001b[0m flat_inputs, captured_inputs\u001b[38;5;241m=\u001b[39mfunction\u001b[38;5;241m.\u001b[39mcaptured_inputs\n\u001b[0;32m 141\u001b[0m )\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[0;32m 1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m 1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m 1320\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m 1321\u001b[0m \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1322\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inference_function\u001b[38;5;241m.\u001b[39mcall_preflattened(args)\n\u001b[0;32m 1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m 1324\u001b[0m args,\n\u001b[0;32m 1325\u001b[0m possible_gradient_type,\n\u001b[0;32m 1326\u001b[0m executing_eagerly)\n\u001b[0;32m 1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 216\u001b[0m flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcall_flat(\u001b[38;5;241m*\u001b[39margs)\n\u001b[0;32m 217\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m 250\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 251\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mcall_function(\n\u001b[0;32m 252\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname,\n\u001b[0;32m 253\u001b[0m \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m 254\u001b[0m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mflat_outputs),\n\u001b[0;32m 255\u001b[0m )\n\u001b[0;32m 256\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 257\u001b[0m outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[0;32m 258\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 259\u001b[0m \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m 260\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[0;32m 261\u001b[0m )\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1552\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m 1550\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m 1551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1552\u001b[0m outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute(\n\u001b[0;32m 1553\u001b[0m name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m 1554\u001b[0m num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m 1555\u001b[0m inputs\u001b[38;5;241m=\u001b[39mtensor_inputs,\n\u001b[0;32m 1556\u001b[0m attrs\u001b[38;5;241m=\u001b[39mattrs,\n\u001b[0;32m 1557\u001b[0m ctx\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 1558\u001b[0m )\n\u001b[0;32m 1559\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1560\u001b[0m outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m 1561\u001b[0m name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m 1562\u001b[0m num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1566\u001b[0m cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m 1567\u001b[0m )\n",
+ "File \u001b[1;32mc:\\Users\\danis\\anaconda3\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 52\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m 54\u001b[0m inputs, attrs, num_outputs)\n\u001b[0;32m 55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 56\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "for layer in base_model.layers[:100]: # Unfreeze the first 100 layers\n",
+ " layer.trainable = True\n",
+ "\n",
+ "optimizer_3 = keras.optimizers.SGD(learning_rate=0.003, momentum=0.9, nesterov=True)\n",
+ "\n",
+ "model_ImagNet.compile(loss=\"categorical_crossentropy\", optimizer=optimizer_3, metrics=[\"accuracy\"])\n",
+ "from keras.callbacks import ModelCheckpoint\n",
+ "checkpoint = ModelCheckpoint(\n",
+ " filepath='model_epoch_{epoch:02d}_val_acc_{val_accuracy:.4f}.keras', # Path to save the model file\n",
+ " monitor='val_accuracy', # Metric to monitor (you can also use 'val_accuracy' or other metrics)\n",
+ " save_best_only=True, # Save only the best model (based on monitored metric)\n",
+ " mode='max', # Minimize the monitored metric (for 'val_loss', use 'min'; for accuracy, use 'max')\n",
+ " save_weights_only=False, # Whether to save the whole model or just the weights\n",
+ " verbose=0 # Display info when saving\n",
+ ")\n",
+ "\n",
+ "history = model_ImagNet.fit(X_train, y_train, epochs=20, batch_size=16, validation_split=0.2, callbacks=[checkpoint])\n",
+ "\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "gt = np.argmax(y_test, axis=1)\n",
+ "y_pred_transfer = model_ImagNet.predict(X_test)\n",
+ "\n",
+ "predictions = np.argmax(y_pred_transfer, axis=1)\n",
+ "confusion_matrix(gt, predictions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 232ms/step\n",
+ "(2000,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "\n",
+ "gt = np.argmax(y_test, axis=1)\n",
+ "y_pred_transfer2 = model_ImagNet.predict(X_test)\n",
+ "\n",
+ "\n",
+ "predictions = np.argmax(y_pred_transfer2, axis=1)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(10000,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(predictions.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "import seaborn as sns\n",
+ "# Compute the confusion matrix\n",
+ "conf_matrix = confusion_matrix(gt, predictions)\n",
+ "# Visualize the confusion matrix to understand model performance across different classes.\n",
+ "\"\"\"\"Rows represent the true labels (the actual class of the images).\n",
+ "Columns represent the predicted labels (the class predicted by your model).\n",
+ "The diagonal values (top-left to bottom-right) represent correctly classified images.\n",
+ "Off-diagonal values represent misclassifications (e.g., if an image from class 2 was classified as class 5).\"\"\"\n",
+ "plt.figure(figsize=(10, 8))\n",
+ "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=label_names, yticklabels=label_names)\n",
+ "plt.xlabel('Predicted Labels')\n",
+ "plt.ylabel('True Labels')\n",
+ "plt.title('Confusion Matrix for CIFAR-10. Transfer Learning: Xception')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From c:\\Users\\danis\\anaconda3\\Lib\\site-packages\\keras\\src\\backend\\common\\global_state.py:82: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Save Model as pickle file\n",
+ "import pickle\n",
+ "with open('CNN_transfer_learning_Xception_Charlie_Dani.pkl', 'wb') as f:\n",
+ " pickle.dump(model, f)\n",
+ " \n",
+ "from keras.backend import clear_session\n",
+ "clear_session()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Top layer consists of average pooling and output layer. So we are removing the top layer\n",
+ "base_model = Xception(weights=\"imagenet\", include_top=False, input_shape=(96, 96, 3))\n",
+ "# Freeze the base model layers\n",
+ "for layer in base_model.layers:\n",
+ " layer.trainable = False\n",
+ "\n",
+ "#And here we are creating our own average pooling and output layer\n",
+ "avg = keras.layers.GlobalAveragePooling2D()(base_model.output)\n",
+ "output = keras.layers.Dense(10, activation=\"softmax\")(avg)\n",
+ "\n",
+ "# Build your custom top layers\n",
+ "custom_top_layers = build_custom_top_layers()\n",
+ "\n",
+ "# Combine the base model and custom top layers\n",
+ "model = Model(inputs=base_model.input, outputs=output)\n",
+ "\n",
+ "# Compile the model\n",
+ "model.compile(optimizer= SGD,\n",
+ " loss='categorical_crossentropy',\n",
+ " metrics=['accuracy'])\n",
+ "\n",
+ "# Summarize the combined model\n",
+ "model.summary()\n",
+ "\n",
+ "# Train the model\n",
+ "history = model.fit(train_generator, \n",
+ " batch_size=batch_size, \n",
+ " epochs=epochs, \n",
+ " validation_split=test_generator, \n",
+ " shuffle=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Get the training and validation accuracy and loss from the history object\n",
+ "training_accuracy = history.history['accuracy']\n",
+ "validation_accuracy = history.history['val_accuracy']\n",
+ "training_loss = history.history['loss']\n",
+ "validation_loss = history.history['val_loss']\n",
+ "\n",
+ "# Plot the training and validation accuracy\n",
+ "plt.plot(training_accuracy)\n",
+ "plt.plot(validation_accuracy)\n",
+ "plt.title('Training and Validation Accuracy of top layer')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.legend(['train', 'validation'], loc='upper left')\n",
+ "plt.show()\n",
+ "\n",
+ "# Plot the training and validation loss\n",
+ "plt.plot(training_loss)\n",
+ "plt.plot(validation_loss)\n",
+ "plt.title('Training and Validation Loss of top layer')\n",
+ "plt.xlabel('Epoch')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.legend(['train', 'validation'], loc='upper left')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/cifar-10-batches-py/batches.meta b/cifar-10-batches-py/batches.meta
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diff --git a/cifar-10-batches-py/data_batch_5 b/cifar-10-batches-py/data_batch_5
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diff --git a/cifar-10-batches-py/readme.html b/cifar-10-batches-py/readme.html
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--- /dev/null
+++ b/cifar-10-batches-py/readme.html
@@ -0,0 +1 @@
+
diff --git a/cifar-10-batches-py/test_batch b/cifar-10-batches-py/test_batch
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..9bf99e37
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,63 @@
+absl-py==2.1.0
+astunparse==1.6.3
+certifi==2024.8.30
+charset-normalizer==3.4.0
+contourpy==1.3.0
+cycler==0.12.1
+filelock==3.16.1
+flatbuffers==24.3.25
+fonttools==4.54.1
+fsspec==2024.9.0
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.67.0
+h5py==3.12.1
+idna==3.10
+Jinja2==3.1.4
+joblib==1.4.2
+keras==3.6.0
+kiwisolver==1.4.7
+libclang==18.1.1
+lightning-utilities==0.11.8
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe==3.0.1
+matplotlib==3.9.2
+mdurl==0.1.2
+ml-dtypes==0.4.1
+mpmath==1.3.0
+namex==0.0.8
+networkx==3.4.1
+numpy==1.26.4
+opencv-python==4.10.0.84
+opt_einsum==3.4.0
+optree==0.13.0
+packaging==24.1
+pandas==2.2.3
+pillow==11.0.0
+protobuf==4.25.5
+Pygments==2.18.0
+pyparsing==3.2.0
+python-dateutil==2.9.0.post0
+pytz==2024.2
+requests==2.32.3
+rich==13.9.2
+scikit-learn==1.5.2
+scipy==1.14.1
+seaborn==0.13.2
+setuptools==75.2.0
+six==1.16.0
+sympy==1.13.1
+tensorboard==2.17.1
+tensorboard-data-server==0.7.2
+termcolor==2.5.0
+threadpoolctl==3.5.0
+torch==2.5.0
+torchmetrics==1.4.3
+torchvision==0.20.0
+typing_extensions==4.12.2
+tzdata==2024.2
+urllib3==2.2.3
+Werkzeug==3.0.4
+wheel==0.44.0
+wrapt==1.16.0