tf2_quickstart_for_experts

tf2_quickstart_for_experts.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "from tensorflow.keras.layers import Dense, Flatten, Conv2D\n",
+    "from tensorflow.keras import Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "mnist = tf.keras.datasets.mnist\n",
+    "\n",
+    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
+    "\n",
+    "x_train = x_train[..., tf.newaxis].astype('float32')\n",
+    "x_test = x_test[..., tf.newaxis].astype('float32')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)\n",
+    "test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [],
+   "source": [
+    "class MyModel(Model):\n",
+    "    def __init__(self):\n",
+    "        super(MyModel, self).__init__()\n",
+    "        self.conv1 = Conv2D(32, 3, activation='relu')\n",
+    "        self.flatten = Flatten()\n",
+    "        self.d1 = Dense(128, activation='relu')\n",
+    "        self.d2 = Dense(10)\n",
+    "\n",
+    "    def call(self, x):\n",
+    "        x = self.conv1(x)\n",
+    "        x = self.flatten(x)\n",
+    "        x = self.d1(x)\n",
+    "        return self.d2(x)\n",
+    "\n",
+    "model = MyModel()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
+    "optimizer = tf.keras.optimizers.Adam()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [],
+   "source": [
+    "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
+    "train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n",
+    "\n",
+    "test_loss = tf.keras.metrics.Mean(name='test_loss')\n",
+    "test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test accuracy')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [],
+   "source": [
+    "@tf.function\n",
+    "def train_step(images, labels):\n",
+    "    with tf.GradientTape() as tape:\n",
+    "        predictions = model(images, training=True)\n",
+    "        loss = loss_object(labels, predictions)\n",
+    "    gradients = tape.gradient(loss, model.trainable_variables)\n",
+    "    optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
+    "\n",
+    "    train_loss(loss)\n",
+    "    train_accuracy(labels, predictions)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [],
+   "source": [
+    "@tf.function\n",
+    "def test_step(images, labels):\n",
+    "    predictions = model(images, training=False)\n",
+    "    t_loss = loss_object(labels, predictions)\n",
+    "\n",
+    "    test_loss(t_loss)\n",
+    "    test_accuracy(labels, predictions)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1, Loss: 0.1377670019865036, Accuracy: 95.8133316040039, Test Loss: 0.06627238541841507, Test Accuracy: 97.80999755859375\n",
+      "Epoch 2, Loss: 0.04135768860578537, Accuracy: 98.72999572753906, Test Loss: 0.06060675159096718, Test Accuracy: 98.11000061035156\n",
+      "Epoch 3, Loss: 0.0216764397919178, Accuracy: 99.2733383178711, Test Loss: 0.05681402236223221, Test Accuracy: 98.36000061035156\n",
+      "Epoch 4, Loss: 0.013888753019273281, Accuracy: 99.5433349609375, Test Loss: 0.058001551777124405, Test Accuracy: 98.3499984741211\n",
+      "Epoch 5, Loss: 0.008770273067057133, Accuracy: 99.70999908447266, Test Loss: 0.05913984403014183, Test Accuracy: 98.38999938964844\n"
+     ]
+    }
+   ],
+   "source": [
+    "EPOCHS = 5\n",
+    "\n",
+    "for epoch in range(EPOCHS):\n",
+    "    train_loss.reset_states()\n",
+    "    train_accuracy.reset_states()\n",
+    "    test_loss.reset_states()\n",
+    "    test_accuracy.reset_states()\n",
+    "\n",
+    "    for images, labels in train_ds:\n",
+    "        train_step(images, labels)\n",
+    "\n",
+    "    for test_images, test_labels in test_ds:\n",
+    "        test_step(test_images, test_labels)\n",
+    "\n",
+    "    print(\n",
+    "    f'Epoch {epoch + 1}, '\n",
+    "    f'Loss: {train_loss.result()}, '\n",
+    "    f'Accuracy: {train_accuracy.result() * 100}, '\n",
+    "    f'Test Loss: {test_loss.result()}, '\n",
+    "    f'Test Accuracy: {test_accuracy.result() * 100}'\n",
+    "  )"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
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