Day 75 of 75 DataSci.ipynb

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     "text": [
      "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
      "170498071/170498071 [==============================] - 55s 0us/step\n",
      "Epoch 1/10\n",
      "  42/1563 [..............................] - ETA: 1:36 - loss: 2.2575 - accuracy: 0.1250"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "\n",
    "# Load CIFAR-10 dataset\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "\n",
    "# Normalize the pixel values to be between 0 and 1\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "\n",
    "# Build the CNN model\n",
    "model = models.Sequential([\n",
    "    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(10)  # 10 classes for CIFAR-10\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))\n",
    "\n",
    "# Evaluate the model\n",
    "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
    "print(f\"Test accuracy: {test_acc}\")\n"
   ]
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