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Chapter01/chap1.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.datasets import mnist\n",
+    "import numpy as np\n",
+    "from keras.models import Sequential\n",
+    "from keras.utils import np_utils\n",
+    "from keras.optimizers import SGD\n",
+    "from keras.layers.core import Dense,Activation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train samples (60000, 28, 28), Train labels (60000,)\n",
+      "Test samples (10000, 28, 28), Test labels (10000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+    "print(\"Train samples {}, Train labels {}\".format(X_train.shape, y_train.shape))\n",
+    "print(\"Test samples {}, Test labels {}\".format(X_test.shape, y_test.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reshape to batch size by height * width\n",
+    "h, w = X_train.shape[1:]\n",
+    "X_train = X_train.reshape(X_train.shape[0], h * w)\n",
+    "X_test = X_test.reshape(X_test.shape[0], h * w)\n",
+    "X_train = X_train.astype('float32')\n",
+    "X_test = X_test.astype('float32')\n",
+    "# scale to [0, 1], scale to [0, 2], offset by -1\n",
+    "X_train = (X_train / 255.0) * 2 - 1\n",
+    "X_test = (X_test - 255.0) * 2 - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert class vectors to a matrix of one-hot vectors\n",
+    "n_classes = 10\n",
+    "y_train = np_utils.to_categorical(y_train, n_classes)\n",
+    "y_test = np_utils.to_categorical(y_test, n_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_1 (Dense)              (None, 128)               100480    \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 10)                1290      \n",
+      "=================================================================\n",
+      "Total params: 101,770\n",
+      "Trainable params: 101,770\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_hidden = 128\n",
+    "model = Sequential()\n",
+    "model.add(Dense(n_hidden, activation='tanh', input_dim=h*w))\n",
+    "model.add(Dense(n_classes, activation='softmax'))\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sgd = SGD(lr=0.001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "60000/60000 [==============================] - 1s 20us/step - loss: 1.7773 - acc: 0.4539\n",
+      "Epoch 2/10\n",
+      "60000/60000 [==============================] - 1s 16us/step - loss: 1.0989 - acc: 0.7330\n",
+      "Epoch 3/10\n",
+      "60000/60000 [==============================] - 1s 16us/step - loss: 0.8551 - acc: 0.7964\n",
+      "Epoch 4/10\n",
+      "60000/60000 [==============================] - 1s 17us/step - loss: 0.7306 - acc: 0.8256: 1s - loss: \n",
+      "Epoch 5/10\n",
+      "60000/60000 [==============================] - 1s 16us/step - loss: 0.6531 - acc: 0.8412\n",
+      "Epoch 6/10\n",
+      "60000/60000 [==============================] - 1s 16us/step - loss: 0.5996 - acc: 0.8526\n",
+      "Epoch 7/10\n",
+      "60000/60000 [==============================] - 1s 16us/step - loss: 0.5600 - acc: 0.8608\n",
+      "Epoch 8/10\n",
+      "60000/60000 [==============================] - 1s 17us/step - loss: 0.5294 - acc: 0.8663\n",
+      "Epoch 9/10\n",
+      "60000/60000 [==============================] - 1s 16us/step - loss: 0.5047 - acc: 0.8712\n",
+      "Epoch 10/10\n",
+      "60000/60000 [==============================] - 1s 16us/step - loss: 0.4844 - acc: 0.8752\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0xd4b5b70>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train, y_train, epochs=10, batch_size=128)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10000/10000 [==============================] - 0s 11us/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = model.evaluate(X_test, y_test, batch_size=128)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}