Upload main.ipynb

main.ipynb

@@ -18,10 +18,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-01-19 18:16:00.702584: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "2025-01-19 18:16:00.727147: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "2025-01-19 18:16:00.732988: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2025-01-19 18:16:00.750369: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "2025-01-19 20:47:16.372082: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2025-01-19 20:47:16.396318: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2025-01-19 20:47:16.402421: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2025-01-19 20:47:16.420317: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
       "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
      ]
     }
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "c5c5e756-0735-4e6d-86a5-33009a44e199",
    "metadata": {},
    "outputs": [
@@ -278,26 +278,36 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/10\n",
-      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 9ms/step - accuracy: 0.8810 - loss: 0.4232\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 8ms/step - accuracy: 0.8804 - loss: 0.4226\n",
       "Epoch 2/10\n",
-      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 9ms/step - accuracy: 0.9648 - loss: 0.1207\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 9ms/step - accuracy: 0.9634 - loss: 0.1228\n",
       "Epoch 3/10\n",
-      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 9ms/step - accuracy: 0.9772 - loss: 0.0790\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 9ms/step - accuracy: 0.9766 - loss: 0.0772\n",
       "Epoch 4/10\n",
-      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9829 - loss: 0.0581\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9813 - loss: 0.0627\n",
       "Epoch 5/10\n",
-      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 8ms/step - accuracy: 0.9875 - loss: 0.0430\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 9ms/step - accuracy: 0.9869 - loss: 0.0447\n",
       "Epoch 6/10\n",
-      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9890 - loss: 0.0365\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 8ms/step - accuracy: 0.9893 - loss: 0.0332\n",
       "Epoch 7/10\n",
-      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 9ms/step - accuracy: 0.9920 - loss: 0.0265\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 9ms/step - accuracy: 0.9926 - loss: 0.0250\n",
       "Epoch 8/10\n",
-      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9930 - loss: 0.0232\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 8ms/step - accuracy: 0.9936 - loss: 0.0224\n",
       "Epoch 9/10\n",
-      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 8ms/step - accuracy: 0.9943 - loss: 0.0182\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9942 - loss: 0.0187\n",
       "Epoch 10/10\n",
-      "\u001b[1m 420/1875\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m11s\u001b[0m 8ms/step - accuracy: 0.9973 - loss: 0.0119"
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9962 - loss: 0.0132\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x781c2256aa10>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -306,10 +316,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "3d879a06-4779-450b-a730-2fd403409392",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9739 - loss: 0.0968\n",
+      "Test accuracy: 0.9768999814987183\n"
+     ]
+    }
+   ],
    "source": [
     "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
     "print('Test accuracy:', test_acc)"
@@ -325,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "5cc13e61-aebf-46e1-94b1-a20d369e0f7d",
    "metadata": {},
    "outputs": [],
@@ -355,12 +374,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "51fdf7c3-0016-4bd6-a99f-9ffebfffd6cf",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x300 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Filter the images to get only those with label '2' for better understanding and comparison\n",
     "label_of_interest = 2\n",
@@ -393,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "0e468ad2-5c10-461d-b39f-38ef3a56b826",
    "metadata": {},
    "outputs": [],
@@ -405,10 +435,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "bb11d7d9-d7a9-495d-ae64-f4609fdbdfbd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 31ms/step - accuracy: 0.6399 - loss: 1.1305\n",
+      "Epoch 2/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 30ms/step - accuracy: 0.7456 - loss: 0.8298\n",
+      "Epoch 3/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 31ms/step - accuracy: 0.7830 - loss: 0.6953\n",
+      "Epoch 4/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m58s\u001b[0m 31ms/step - accuracy: 0.8088 - loss: 0.6210\n",
+      "Epoch 5/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 30ms/step - accuracy: 0.8225 - loss: 0.5732\n",
+      "Epoch 6/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 30ms/step - accuracy: 0.8357 - loss: 0.5396\n",
+      "Epoch 7/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 31ms/step - accuracy: 0.8414 - loss: 0.5202\n",
+      "Epoch 8/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 28ms/step - accuracy: 0.8482 - loss: 0.5012\n",
+      "Epoch 9/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 29ms/step - accuracy: 0.8566 - loss: 0.4772\n",
+      "Epoch 10/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 30ms/step - accuracy: 0.8574 - loss: 0.4629\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x781c1ecec190>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Train the model using the augmented data generator\n",
     "model.fit(datagen.flow(train_images, train_labels, batch_size=32), epochs=10)"
@@ -416,10 +483,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "5d4bbd2a-9e18-49dd-a555-d1841d5d68f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9229 - loss: 0.2424\n",
+      "Augmented test accuracy: 0.932200014591217\n"
+     ]
+    }
+   ],
    "source": [
     "# Evaluate the model on the test set\n",
     "augmented_test_loss, augmented_test_acc = model.evaluate(test_images, test_labels)\n",
@@ -428,10 +504,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "9a788df7-c878-49f6-86b5-e385be7bf842",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simple test accuracy: 0.9768999814987183\n",
+      "Simple augmented test accuracy: 0.932200014591217\n"
+     ]
+    }
+   ],
    "source": [
     "# Compare the model accuracy on the original set vs the augmented set\n",
     "print('Simple test accuracy:', test_acc)\n",
@@ -448,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "89cca745-bda4-4a8a-92f0-8d31d508cc8e",
    "metadata": {},
    "outputs": [],
@@ -474,7 +559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "c1dfe2cf-86f9-4f36-8184-1dd566ece339",
    "metadata": {},
    "outputs": [],
@@ -484,22 +569,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "93ba283c-6a0f-49c8-adf6-505ac51cbece",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m251s\u001b[0m 133ms/step - accuracy: 0.5385 - loss: 1.3305 - val_accuracy: 0.9559 - val_loss: 0.1485\n",
+      "Epoch 2/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m246s\u001b[0m 131ms/step - accuracy: 0.8474 - loss: 0.4887 - val_accuracy: 0.9647 - val_loss: 0.1102\n",
+      "Epoch 3/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m241s\u001b[0m 129ms/step - accuracy: 0.8866 - loss: 0.3713 - val_accuracy: 0.9685 - val_loss: 0.0905\n",
+      "Epoch 4/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m241s\u001b[0m 128ms/step - accuracy: 0.9064 - loss: 0.3077 - val_accuracy: 0.9735 - val_loss: 0.0727\n",
+      "Epoch 5/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m245s\u001b[0m 130ms/step - accuracy: 0.9166 - loss: 0.2790 - val_accuracy: 0.9762 - val_loss: 0.0756\n",
+      "Epoch 6/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 129ms/step - accuracy: 0.9195 - loss: 0.2690 - val_accuracy: 0.9792 - val_loss: 0.0639\n",
+      "Epoch 7/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m245s\u001b[0m 130ms/step - accuracy: 0.9270 - loss: 0.2395 - val_accuracy: 0.9804 - val_loss: 0.0569\n",
+      "Epoch 8/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m240s\u001b[0m 128ms/step - accuracy: 0.9340 - loss: 0.2231 - val_accuracy: 0.9774 - val_loss: 0.0704\n",
+      "Epoch 9/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 130ms/step - accuracy: 0.9356 - loss: 0.2144 - val_accuracy: 0.9844 - val_loss: 0.0496\n",
+      "Epoch 10/10\n",
+      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m240s\u001b[0m 128ms/step - accuracy: 0.9395 - loss: 0.2053 - val_accuracy: 0.9827 - val_loss: 0.0538\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x781c1ebabb10>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "advanced_model.fit(datagen.flow(train_images, train_labels, batch_size=128),\n",
+    "advanced_model.fit(datagen.flow(train_images, train_labels, batch_size=32),\n",
     "          validation_data=(test_images, test_labels),\n",
     "          epochs=10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "ba5a04bc-f912-45ee-8c38-f860ae59687a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 28ms/step - accuracy: 0.9814 - loss: 0.0564\n",
+      "Simple test accuracy: 0.9768999814987183\n",
+      "Simple augmented test accuracy: 0.932200014591217\n",
+      "Advanced test accuracy: 0.982699990272522\n"
+     ]
+    }
+   ],
    "source": [
     "advanced_test_loss, advanced_test_acc = advanced_model.evaluate(test_images, test_labels)\n",
     "print('Simple test accuracy:', test_acc)\n",
@@ -509,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "2178138b-5388-45a0-9e01-5cbae10e5bb9",
    "metadata": {},
    "outputs": [],
@@ -518,14 +651,6 @@
     "if __name__ == \"__main__\":\n",
     "    pass  # Prevent unintended execution during import"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9b99f734-21dd-4621-b62f-8f2a88783f0b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {