{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1dn3r8mHLUi5sYCMgbnwU2KrBu_Y02sDc","authorship_tag":"ABX9TyO0tvxugPJ/76DNMPvDb9lR"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"N53OL9uswOGJ","executionInfo":{"status":"ok","timestamp":1762897674712,"user_tz":0,"elapsed":8150825,"user":{"displayName":"Khawja redwanul islam","userId":"08565280838879170063"}},"outputId":"2e338603-af7b-45ba-e36a-9d10efe6e476"},"outputs":[{"output_type":"stream","name":"stdout","text":["Loading CIFAR-10 dataset...\n","Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n","\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n","Creating validation split...\n","Training images shape: (40000, 32, 32, 3)\n","Validation images shape: (10000, 32, 32, 3)\n","Test images shape: (10000, 32, 32, 3)\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: 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"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"Optimized_CIFAR10_CNN\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"Optimized_CIFAR10_CNN\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"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","│ 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","│ conv2d_1 (\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","│ 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","│ conv2d_2 (\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","│ 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","│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\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;34m128\u001b[0m)            │       \u001b[38;5;34m262,272\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\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;34m1,290\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,272</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m356,810\u001b[0m (1.36 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">356,810</span> (1.36 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m356,810\u001b[0m (1.36 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">356,810</span> (1.36 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Starting model training with data augmentation...\n","Epoch 1/100\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n","  self._warn_if_super_not_called()\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - accuracy: 0.2456 - loss: 2.0253\n","Epoch 1: val_accuracy improved from -inf to 0.44560, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 196ms/step - accuracy: 0.2457 - loss: 2.0251 - val_accuracy: 0.4456 - val_loss: 1.5142\n","Epoch 2/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - accuracy: 0.4066 - loss: 1.6431\n","Epoch 2: val_accuracy improved from 0.44560 to 0.52590, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 185ms/step - accuracy: 0.4066 - loss: 1.6431 - val_accuracy: 0.5259 - val_loss: 1.3192\n","Epoch 3/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.4549 - loss: 1.5080\n","Epoch 3: val_accuracy improved from 0.52590 to 0.54800, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 182ms/step - accuracy: 0.4549 - loss: 1.5080 - val_accuracy: 0.5480 - val_loss: 1.2565\n","Epoch 4/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.4789 - loss: 1.4372\n","Epoch 4: val_accuracy improved from 0.54800 to 0.60200, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 181ms/step - accuracy: 0.4789 - loss: 1.4372 - val_accuracy: 0.6020 - val_loss: 1.1036\n","Epoch 5/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.5214 - loss: 1.3568\n","Epoch 5: val_accuracy did not improve from 0.60200\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 184ms/step - accuracy: 0.5214 - loss: 1.3568 - val_accuracy: 0.5935 - val_loss: 1.1589\n","Epoch 6/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.5424 - loss: 1.2937\n","Epoch 6: val_accuracy improved from 0.60200 to 0.60650, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 180ms/step - accuracy: 0.5424 - loss: 1.2937 - val_accuracy: 0.6065 - val_loss: 1.1040\n","Epoch 7/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.5579 - loss: 1.2521\n","Epoch 7: val_accuracy improved from 0.60650 to 0.62700, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 180ms/step - accuracy: 0.5579 - loss: 1.2521 - val_accuracy: 0.6270 - val_loss: 1.0471\n","Epoch 8/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.5790 - loss: 1.1920\n","Epoch 8: val_accuracy improved from 0.62700 to 0.62820, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 185ms/step - accuracy: 0.5790 - loss: 1.1920 - val_accuracy: 0.6282 - val_loss: 1.0593\n","Epoch 9/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.5871 - loss: 1.1830\n","Epoch 9: val_accuracy improved from 0.62820 to 0.66610, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 185ms/step - accuracy: 0.5871 - loss: 1.1830 - val_accuracy: 0.6661 - val_loss: 0.9588\n","Epoch 10/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.5958 - loss: 1.1503\n","Epoch 10: val_accuracy did not improve from 0.66610\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 183ms/step - accuracy: 0.5958 - loss: 1.1503 - val_accuracy: 0.6628 - val_loss: 0.9632\n","Epoch 11/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.6059 - loss: 1.1277\n","Epoch 11: val_accuracy improved from 0.66610 to 0.69120, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 178ms/step - accuracy: 0.6059 - loss: 1.1277 - val_accuracy: 0.6912 - val_loss: 0.8749\n","Epoch 12/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.6147 - loss: 1.0945\n","Epoch 12: val_accuracy improved from 0.69120 to 0.69710, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 180ms/step - accuracy: 0.6147 - loss: 1.0945 - val_accuracy: 0.6971 - val_loss: 0.8646\n","Epoch 13/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.6250 - loss: 1.0757\n","Epoch 13: val_accuracy did not improve from 0.69710\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 186ms/step - accuracy: 0.6250 - loss: 1.0757 - val_accuracy: 0.6853 - val_loss: 0.8907\n","Epoch 14/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.6326 - loss: 1.0577\n","Epoch 14: val_accuracy did not improve from 0.69710\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 185ms/step - accuracy: 0.6326 - loss: 1.0577 - val_accuracy: 0.6784 - val_loss: 0.9207\n","Epoch 15/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - accuracy: 0.6331 - loss: 1.0540\n","Epoch 15: val_accuracy did not improve from 0.69710\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 189ms/step - accuracy: 0.6331 - loss: 1.0539 - val_accuracy: 0.6954 - val_loss: 0.8835\n","Epoch 16/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - accuracy: 0.6450 - loss: 1.0246\n","Epoch 16: val_accuracy did not improve from 0.69710\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 186ms/step - accuracy: 0.6450 - loss: 1.0246 - val_accuracy: 0.6931 - val_loss: 0.8729\n","Epoch 17/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - accuracy: 0.6441 - loss: 1.0234\n","Epoch 17: val_accuracy did not improve from 0.69710\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 181ms/step - accuracy: 0.6441 - loss: 1.0234 - val_accuracy: 0.6817 - val_loss: 0.9338\n","Epoch 18/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 0.6534 - loss: 1.0021\n","Epoch 18: val_accuracy improved from 0.69710 to 0.73780, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m119s\u001b[0m 189ms/step - accuracy: 0.6534 - loss: 1.0021 - val_accuracy: 0.7378 - val_loss: 0.7527\n","Epoch 19/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.6571 - loss: 0.9933\n","Epoch 19: val_accuracy did not improve from 0.73780\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 187ms/step - accuracy: 0.6571 - loss: 0.9933 - val_accuracy: 0.7241 - val_loss: 0.7826\n","Epoch 20/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.6584 - loss: 0.9791\n","Epoch 20: val_accuracy did not improve from 0.73780\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 179ms/step - accuracy: 0.6584 - loss: 0.9791 - val_accuracy: 0.7009 - val_loss: 0.8586\n","Epoch 21/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.6596 - loss: 0.9900\n","Epoch 21: val_accuracy did not improve from 0.73780\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 179ms/step - accuracy: 0.6596 - loss: 0.9899 - val_accuracy: 0.7356 - val_loss: 0.7531\n","Epoch 22/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.6767 - loss: 0.9499\n","Epoch 22: val_accuracy did not improve from 0.73780\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 180ms/step - accuracy: 0.6767 - loss: 0.9499 - val_accuracy: 0.7152 - val_loss: 0.8438\n","Epoch 23/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.6753 - loss: 0.9391\n","Epoch 23: val_accuracy did not improve from 0.73780\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 186ms/step - accuracy: 0.6753 - loss: 0.9391 - val_accuracy: 0.7267 - val_loss: 0.8050\n","Epoch 24/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.6830 - loss: 0.9263\n","Epoch 24: val_accuracy improved from 0.73780 to 0.74100, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 185ms/step - accuracy: 0.6830 - loss: 0.9263 - val_accuracy: 0.7410 - val_loss: 0.7506\n","Epoch 25/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.6841 - loss: 0.9234\n","Epoch 25: val_accuracy did not improve from 0.74100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m139s\u001b[0m 180ms/step - accuracy: 0.6841 - loss: 0.9234 - val_accuracy: 0.7196 - val_loss: 0.8092\n","Epoch 26/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.6897 - loss: 0.9051\n","Epoch 26: val_accuracy did not improve from 0.74100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 180ms/step - accuracy: 0.6897 - loss: 0.9051 - val_accuracy: 0.6979 - val_loss: 0.8823\n","Epoch 27/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.6899 - loss: 0.9098\n","Epoch 27: val_accuracy did not improve from 0.74100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 180ms/step - accuracy: 0.6899 - loss: 0.9098 - val_accuracy: 0.7410 - val_loss: 0.7510\n","Epoch 28/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.6861 - loss: 0.9153\n","Epoch 28: val_accuracy improved from 0.74100 to 0.74940, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 178ms/step - accuracy: 0.6861 - loss: 0.9153 - val_accuracy: 0.7494 - val_loss: 0.7260\n","Epoch 29/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.6893 - loss: 0.9043\n","Epoch 29: val_accuracy improved from 0.74940 to 0.75360, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 180ms/step - accuracy: 0.6893 - loss: 0.9043 - val_accuracy: 0.7536 - val_loss: 0.7112\n","Epoch 30/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.6909 - loss: 0.8968\n","Epoch 30: val_accuracy did not improve from 0.75360\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 180ms/step - accuracy: 0.6909 - loss: 0.8968 - val_accuracy: 0.7499 - val_loss: 0.7243\n","Epoch 31/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.6955 - loss: 0.8932\n","Epoch 31: val_accuracy did not improve from 0.75360\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 177ms/step - accuracy: 0.6955 - loss: 0.8932 - val_accuracy: 0.7384 - val_loss: 0.7584\n","Epoch 32/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7058 - loss: 0.8652\n","Epoch 32: val_accuracy did not improve from 0.75360\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 185ms/step - accuracy: 0.7057 - loss: 0.8652 - val_accuracy: 0.7337 - val_loss: 0.7659\n","Epoch 33/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7035 - loss: 0.8680\n","Epoch 33: val_accuracy did not improve from 0.75360\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 179ms/step - accuracy: 0.7035 - loss: 0.8680 - val_accuracy: 0.7362 - val_loss: 0.7647\n","Epoch 34/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.7004 - loss: 0.8763\n","Epoch 34: val_accuracy improved from 0.75360 to 0.75670, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 179ms/step - accuracy: 0.7004 - loss: 0.8762 - val_accuracy: 0.7567 - val_loss: 0.7158\n","Epoch 35/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.6994 - loss: 0.8680\n","Epoch 35: val_accuracy did not improve from 0.75670\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 178ms/step - accuracy: 0.6994 - loss: 0.8680 - val_accuracy: 0.7520 - val_loss: 0.7312\n","Epoch 36/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.7067 - loss: 0.8567\n","Epoch 36: val_accuracy did not improve from 0.75670\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 180ms/step - accuracy: 0.7067 - loss: 0.8568 - val_accuracy: 0.7314 - val_loss: 0.7676\n","Epoch 37/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - accuracy: 0.6989 - loss: 0.8720\n","Epoch 37: val_accuracy did not improve from 0.75670\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 188ms/step - accuracy: 0.6989 - loss: 0.8720 - val_accuracy: 0.7527 - val_loss: 0.7115\n","Epoch 38/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.7082 - loss: 0.8489\n","Epoch 38: val_accuracy improved from 0.75670 to 0.75980, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 181ms/step - accuracy: 0.7081 - loss: 0.8489 - val_accuracy: 0.7598 - val_loss: 0.6988\n","Epoch 39/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7112 - loss: 0.8393\n","Epoch 39: val_accuracy did not improve from 0.75980\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 181ms/step - accuracy: 0.7112 - loss: 0.8393 - val_accuracy: 0.7465 - val_loss: 0.7425\n","Epoch 40/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.7137 - loss: 0.8415\n","Epoch 40: val_accuracy did not improve from 0.75980\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 178ms/step - accuracy: 0.7137 - loss: 0.8415 - val_accuracy: 0.7447 - val_loss: 0.7448\n","Epoch 41/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - accuracy: 0.7179 - loss: 0.8318\n","Epoch 41: val_accuracy did not improve from 0.75980\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 181ms/step - accuracy: 0.7179 - loss: 0.8318 - val_accuracy: 0.7567 - val_loss: 0.7207\n","Epoch 42/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.7147 - loss: 0.8372\n","Epoch 42: val_accuracy improved from 0.75980 to 0.76290, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 182ms/step - accuracy: 0.7147 - loss: 0.8372 - val_accuracy: 0.7629 - val_loss: 0.7141\n","Epoch 43/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.7163 - loss: 0.8335\n","Epoch 43: val_accuracy did not improve from 0.76290\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 183ms/step - accuracy: 0.7163 - loss: 0.8335 - val_accuracy: 0.7546 - val_loss: 0.7414\n","Epoch 44/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.7163 - loss: 0.8257\n","Epoch 44: val_accuracy improved from 0.76290 to 0.76780, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m140s\u001b[0m 179ms/step - accuracy: 0.7163 - loss: 0.8257 - val_accuracy: 0.7678 - val_loss: 0.6922\n","Epoch 45/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7186 - loss: 0.8127\n","Epoch 45: val_accuracy improved from 0.76780 to 0.77050, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 186ms/step - accuracy: 0.7186 - loss: 0.8127 - val_accuracy: 0.7705 - val_loss: 0.6841\n","Epoch 46/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7172 - loss: 0.8226\n","Epoch 46: val_accuracy did not improve from 0.77050\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 178ms/step - accuracy: 0.7172 - loss: 0.8226 - val_accuracy: 0.7658 - val_loss: 0.6918\n","Epoch 47/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7227 - loss: 0.8049\n","Epoch 47: val_accuracy improved from 0.77050 to 0.77740, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 185ms/step - accuracy: 0.7227 - loss: 0.8049 - val_accuracy: 0.7774 - val_loss: 0.6638\n","Epoch 48/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.7252 - loss: 0.8102\n","Epoch 48: val_accuracy did not improve from 0.77740\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 181ms/step - accuracy: 0.7252 - loss: 0.8102 - val_accuracy: 0.7598 - val_loss: 0.7150\n","Epoch 49/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7250 - loss: 0.8021\n","Epoch 49: val_accuracy did not improve from 0.77740\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 179ms/step - accuracy: 0.7250 - loss: 0.8021 - val_accuracy: 0.7735 - val_loss: 0.6588\n","Epoch 50/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.7269 - loss: 0.7980\n","Epoch 50: val_accuracy did not improve from 0.77740\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m117s\u001b[0m 187ms/step - accuracy: 0.7269 - loss: 0.7980 - val_accuracy: 0.7443 - val_loss: 0.7657\n","Epoch 51/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.7300 - loss: 0.7970\n","Epoch 51: val_accuracy did not improve from 0.77740\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 181ms/step - accuracy: 0.7300 - loss: 0.7970 - val_accuracy: 0.7613 - val_loss: 0.7239\n","Epoch 52/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.7270 - loss: 0.7940\n","Epoch 52: val_accuracy did not improve from 0.77740\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 182ms/step - accuracy: 0.7270 - loss: 0.7940 - val_accuracy: 0.7638 - val_loss: 0.7011\n","Epoch 53/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7305 - loss: 0.7930\n","Epoch 53: val_accuracy did not improve from 0.77740\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 186ms/step - accuracy: 0.7305 - loss: 0.7930 - val_accuracy: 0.7700 - val_loss: 0.6712\n","Epoch 54/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7314 - loss: 0.7887\n","Epoch 54: val_accuracy did not improve from 0.77740\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m112s\u001b[0m 179ms/step - accuracy: 0.7314 - loss: 0.7887 - val_accuracy: 0.7611 - val_loss: 0.7232\n","Epoch 55/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.7356 - loss: 0.7876\n","Epoch 55: val_accuracy improved from 0.77740 to 0.79170, saving model to best_cifar_model.keras\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 180ms/step - accuracy: 0.7356 - loss: 0.7876 - val_accuracy: 0.7917 - val_loss: 0.6211\n","Epoch 56/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7318 - loss: 0.7840\n","Epoch 56: val_accuracy did not improve from 0.79170\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 180ms/step - accuracy: 0.7318 - loss: 0.7841 - val_accuracy: 0.7479 - val_loss: 0.7521\n","Epoch 57/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7320 - loss: 0.7856\n","Epoch 57: val_accuracy did not improve from 0.79170\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 181ms/step - accuracy: 0.7320 - loss: 0.7856 - val_accuracy: 0.7765 - val_loss: 0.6612\n","Epoch 58/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7360 - loss: 0.7860\n","Epoch 58: val_accuracy did not improve from 0.79170\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 185ms/step - accuracy: 0.7360 - loss: 0.7860 - val_accuracy: 0.7763 - val_loss: 0.6633\n","Epoch 59/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7395 - loss: 0.7658\n","Epoch 59: val_accuracy did not improve from 0.79170\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 178ms/step - accuracy: 0.7395 - loss: 0.7658 - val_accuracy: 0.7790 - val_loss: 0.6542\n","Epoch 60/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7334 - loss: 0.7814\n","Epoch 60: val_accuracy did not improve from 0.79170\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 181ms/step - accuracy: 0.7334 - loss: 0.7814 - val_accuracy: 0.7745 - val_loss: 0.6546\n","Epoch 61/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - accuracy: 0.7401 - loss: 0.7651\n","Epoch 61: val_accuracy did not improve from 0.79170\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 178ms/step - accuracy: 0.7401 - loss: 0.7651 - val_accuracy: 0.7884 - val_loss: 0.6211\n","Epoch 62/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - accuracy: 0.7367 - loss: 0.7702\n","Epoch 62: val_accuracy did not improve from 0.79170\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 181ms/step - accuracy: 0.7367 - loss: 0.7702 - val_accuracy: 0.7647 - val_loss: 0.7090\n","Epoch 63/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.7354 - loss: 0.7777\n","Epoch 63: val_accuracy did not improve from 0.79170\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m145s\u001b[0m 186ms/step - accuracy: 0.7354 - loss: 0.7777 - val_accuracy: 0.7683 - val_loss: 0.7192\n","Epoch 64/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.7409 - loss: 0.7589\n","Epoch 64: val_accuracy did not improve from 0.79170\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 188ms/step - accuracy: 0.7409 - loss: 0.7589 - val_accuracy: 0.7844 - val_loss: 0.6426\n","Epoch 65/100\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.7420 - loss: 0.7553\n","Epoch 65: val_accuracy did not improve from 0.79170\n","\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 188ms/step - accuracy: 0.7420 - loss: 0.7553 - val_accuracy: 0.7723 - val_loss: 0.6726\n","Epoch 65: early stopping\n","Restoring model weights from the end of the best epoch: 55.\n","Training finished. Evaluating on test set...\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 24ms/step - accuracy: 0.7870 - loss: 0.6550\n","\n","Test Accuracy: 78.42%\n","Test Loss: 0.6411\n","Generating predictions for test set...\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 22ms/step\n","Generating confusion matrix...\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x800 with 2 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Generating prediction examples...\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x1000 with 25 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Generating training history plots...\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Script complete. All plots saved to disk.\n"]}],"source":["import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","from tensorflow.keras.datasets import cifar10\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix\n","import seaborn as sns\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","# 1. Load and Preprocess the CIFAR-10 Data\n","print(\"Loading CIFAR-10 dataset...\")\n","(x_train_full, y_train_full), (x_test, y_test) = cifar10.load_data()\n","\n","# Define class names for plots\n","class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n","               'dog', 'frog', 'horse', 'ship', 'truck']\n","\n","# Normalize pixel values from 0-255 to 0-1\n","x_train_full = x_train_full.astype('float32') / 255.0\n","x_test = x_test.astype('float32') / 255.0\n","\n","# Convert labels to one-hot encoding\n","num_classes = 10\n","y_train_full = keras.utils.to_categorical(y_train_full, num_classes)\n","y_test = keras.utils.to_categorical(y_test, num_classes)\n","\n","# --- 2. Create a Validation Set ---\n","# Split the full training data into training and validation sets\n","print(\"Creating validation split...\")\n","x_train, x_val, y_train, y_val = train_test_split(\n","    x_train_full, y_train_full, test_size=0.2, random_state=42\n",")\n","\n","print(f\"Training images shape: {x_train.shape}\")\n","print(f\"Validation images shape: {x_val.shape}\")\n","print(f\"Test images shape: {x_test.shape}\")\n","\n","# 3.CNN Model Architecture\n","model = keras.Sequential(name=\"Optimized_CIFAR10_CNN\")\n","\n","# Input Block\n","model.add(layers.Conv2D(32, (3, 3), activation='relu', padding='same',\n","                         input_shape=(32, 32, 3)))\n","model.add(layers.MaxPooling2D((2, 2)))\n","\n","# Feature Extraction Block 2\n","model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))\n","model.add(layers.MaxPooling2D((2, 2)))\n","\n","# Feature Extraction Block 3\n","model.add(layers.Conv2D(128, (3, 3), activation='relu', padding='same'))\n","model.add(layers.MaxPooling2D((2, 2)))\n","\n","# Classifier Head\n","model.add(layers.Flatten())\n","model.add(layers.Dense(128, activation='relu'))\n","model.add(layers.Dropout(0.5)) # Key regularization step\n","model.add(layers.Dense(num_classes, activation='softmax')) # Output layer for 10 classes\n","\n","model.summary()\n","\n","# --- 4. Set Up Data Augmentation ---\n","# Creates more varied images to prevent overfitting\n","datagen = ImageDataGenerator(\n","    rotation_range=20,      # Randomly rotate images\n","    width_shift_range=0.2,  # Randomly shift images horizontally\n","    height_shift_range=0.2, # Randomly shift images vertically\n","    horizontal_flip=True,   # Randomly flip images horizontally\n","    zoom_range=0.2,         # Randomly zoom in on images\n",")\n","\n","\n","# 5. Compile the Model\n","# Configures the model for training\n","model.compile(\n","    optimizer='adam',                # Adam optimizer is efficient\n","    loss='categorical_crossentropy', # For one-hot encoded labels\n","    metrics=['accuracy']\n",")\n","\n","# 6. Define Callbacks\n","# Callbacks monitor training and take actions\n","checkpoint = ModelCheckpoint(\n","    \"best_cifar_model.keras\", # Save the best model\n","    monitor=\"val_accuracy\",\n","    save_best_only=True,\n","    verbose=1\n",")\n","\n","early_stop = EarlyStopping(\n","    monitor=\"val_loss\",\n","    patience=10, # Stop if val_loss doesn't improve for 10 epochs\n","    restore_best_weights=True,\n","    verbose=1\n",")\n","\n","callbacks_list = [checkpoint, early_stop]\n","\n","# 7. Train the Model\n","batch_size = 64\n","epochs = 100 # Set high, as EarlyStopping will find the optimal number\n","\n","print(\"Starting model training with data augmentation...\")\n","history = model.fit(\n","    datagen.flow(x_train, y_train, batch_size=batch_size), # Use the generator\n","    steps_per_epoch=len(x_train) // batch_size, # Batches per epoch\n","    epochs=epochs,\n","    validation_data=(x_val, y_val), # Use the static validation set\n","    callbacks=callbacks_list\n",")\n","\n","# 8. Evaluate and Plot Results\n","print(\"Training finished. Evaluating on test set...\")\n","\n","# Load the best performing model saved by ModelCheckpoint\n","best_model = keras.models.load_model(\"best_cifar_model.keras\")\n","\n","# Evaluate on the unseen test set\n","test_loss, test_acc = best_model.evaluate(x_test, y_test)\n","print(f\"\\nTest Accuracy: {test_acc*100:.2f}%\")\n","print(f\"Test Loss: {test_loss:.4f}\")\n","\n","# 9. Generate Predictions, Confusion Matrix, and Examples\n","print(\"Generating predictions for test set...\")\n","# Predict probabilities for the test set\n","y_pred_probs = best_model.predict(x_test)\n","# Convert probabilities to class labels (index of the max value)\n","y_pred_labels = np.argmax(y_pred_probs, axis=1)\n","# Convert one-hot test labels back to single-digit labels\n","y_true_labels = np.argmax(y_test, axis=1)\n","\n","# --- Plot Confusion Matrix ---\n","print(\"Generating confusion matrix...\")\n","cm = confusion_matrix(y_true_labels, y_pred_labels)\n","\n","plt.figure(figsize=(10, 8))\n","sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n","            xticklabels=class_names, yticklabels=class_names)\n","plt.xlabel('Predicted Label')\n","plt.ylabel('True Label')\n","plt.title('Confusion Matrix')\n","plt.savefig('confusion_matrix.png')\n","plt.show()\n","\n","# Show Prediction Examples\n","print(\"Generating prediction examples...\")\n","plt.figure(figsize=(10, 10))\n","for i in range(25):\n","    plt.subplot(5, 5, i + 1)\n","    plt.xticks([])\n","    plt.yticks([])\n","    plt.grid(False)\n","    # x_test is already normalized (0-1), which imshow handles\n","    plt.imshow(x_test[i])\n","\n","    pred_label = class_names[y_pred_labels[i]]\n","    true_label = class_names[y_true_labels[i]]\n","\n","    # Set label color to green for correct, red for incorrect\n","    color = 'green' if pred_label == true_label else 'red'\n","    plt.xlabel(f\"Pred: {pred_label}\\nTrue: {true_label}\", color=color)\n","\n","plt.tight_layout()\n","plt.savefig('prediction_examples.png')\n","plt.show()\n","\n","# Plotting Training History (for the report)\n","print(\"Generating training history plots...\")\n","plt.figure(figsize=(12, 4))\n","\n","# Plot Training & Validation Accuracy\n","plt.subplot(1, 2, 1)\n","plt.plot(history.history['accuracy'], label='Training Accuracy')\n","plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n","plt.title('Model Accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend(loc='lower right')\n","\n","# Plot Training & Validation Loss\n","plt.subplot(1, 2, 2)\n","plt.plot(history.history['loss'], label='Training Loss')\n","plt.plot(history.history['val_loss'], label='Validation Loss')\n","plt.title('Model Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend(loc='upper right')\n","\n","plt.suptitle('Model Training History')\n","plt.savefig('model_history.png')\n","plt.show()\n","\n","print(\"Script complete. All plots saved to disk.\")"]}]}