前回の記事でようやくDeepっぽいところまで来たので、そのままさっさとDeepらしさの象徴でもあるCNN (Convolutional Neural Network)にいってしまおうと思います。ちなみに今回も大して参照していませんが、参考文献として深層学習青本を掲げておきます。
- 作者: 岡谷貴之
- 出版社/メーカー: 講談社
- 発売日: 2015/04/08
- メディア: 単行本（ソフトカバー）
- この商品を含むブログ (13件) を見る
それでは、いよいよCNNをやってみましょう。
生TensorFlowでLeNetを回してみる
ようやくCNNをやってみるわけですが、実はTensorFlow公式に立派なチュートリアルがあります(Deep MNIST for Experts | TensorFlow)。というか、MNIST + CNNというところまで来ればweb上の至るところにいくらでも「やってみました」系の記事が大量に転がっています。むしろこれまではどこにも参考になる資料がなくて困ったというのが実態だったりします（笑）。ということで、これをなぞればおしまいです*1。使ったデータは前回同様簡易版MNISTです。
import tensorflow as tf import numpy as np import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score d_train = pd.read_csv("short_prac_train.csv", sep=',') d_test = pd.read_csv("short_prac_test.csv", sep=',') train_X = d_train.iloc[:, 1:785]/255 train_Y = d_train[[0]] test_X = d_test.iloc[:, 1:785]/255 test_Y = d_test[[0]] # Define input x = tf.placeholder(tf.float32, [None, 784]) # Turn into CNN # For simplicity, just follow "Deep MNIST for Experts" def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides = [1, 1, 1, 1], padding = 'SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME') # Just accumulate layers as GoogeLenet # 1st layer W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 28, 28, 1]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) # 2nd layer W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) # Dense W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) # Dropout keep_prob = tf.placeholder(tf.float32) # set 1.0 if inference h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) # Readout W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 # Define label & momentum optimizer y = tf.placeholder(tf.int64, [None, 1]) y_ = tf.one_hot(indices = y, depth = 10) global_step = tf.Variable(0, trainable=False) starter_learning_rate = 0.01 learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step, 10000, 1 - 1e-6, staircase=True) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y_conv)) optimizer = tf.train.MomentumOptimizer(learning_rate, momentum = 0.9, use_nesterov=True).minimize(cost, global_step = global_step) # WITH minibatch init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) num_epochs = 30 num_data = train_X.shape[0] batch_size = 50 for epoch in range(num_epochs): s_idx = np.random.permutation(num_data) for idx in range(0, num_data, batch_size): batch_x = train_X.iloc[s_idx[idx: idx + batch_size].tolist(),:] batch_y = train_Y.iloc[s_idx[idx: idx + batch_size].tolist()] sess.run(optimizer, feed_dict={x:batch_x, y:batch_y, keep_prob:0.5}) pred = sess.run(y_conv, feed_dict = {x: test_X, keep_prob:1.0}) pred_d = tf.argmax(pred,1) from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score print confusion_matrix(test_Y, sess.run(pred_d)), accuracy_score(test_Y, sess.run(pred_d))
[[ 97 0 0 0 0 0 2 0 1 0] [ 0 99 0 0 0 0 0 0 1 0] [ 0 0 99 0 0 0 0 1 0 0] [ 0 0 0 99 0 0 0 0 1 0] [ 0 2 0 0 97 0 0 0 0 1] [ 0 0 0 0 0 100 0 0 0 0] [ 0 0 0 0 0 1 98 0 1 0] [ 0 0 0 0 0 0 0 100 0 0] [ 0 0 1 0 1 0 0 0 98 0] [ 0 0 0 0 1 0 0 0 0 99]] 0.986
いとも簡単にACC 0.986が出ました（驚）。このチュートリアルのコードはKerasで書いたりするのに比べればまだるっこしいんですが、それでもconv層のところを書きやすくするために事前に関数を定義して簡単に書けるようにしてあったり、keep_probの値次第でtrainingとinferenceを分けられるようにしてあったり、そこそこ親切だという印象です。
Keras in Rで同じくLeNetを回してみる
前回も紹介した以前の記事に、ほぼ同じ設定のCNNをKerasで回した例があります。
以下そのまま転載しておきます。
library(keras) train <- read.csv('short_mnist_train.csv', header=T, sep=',') test <- read.csv('short_mnist_test.csv', header=T, sep=',') x_train <- array(<span style="color: #ff0000">as.matrix</span>(train[,-1]/255), dim=c(5000, 28, 28, 1)) y_train <- train[,1] %>% to_categorical(num_classes = 10) x_test <- array(<span style="color: #ff0000">as.matrix</span>(test[,-1]/255), dim=c(1000, 28, 28, 1)) y_test <- test[,1] %>% to_categorical(num_classes = 10) # create model model <- keras_model_sequential() model %>% layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu', input_shape = c(28,28, 1)) %>% layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>% layer_max_pooling_2d(pool_size = c(2,2)) %>% layer_dropout(rate = 0.25) %>% layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>% layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>% layer_max_pooling_2d(pool_size = c(2,2)) %>% layer_dropout(rate = 0.25) %>% layer_flatten() %>% layer_dense(units = 128, activation = 'relu') %>% layer_dropout(rate = 0.25) %>% layer_dense(units = 10, activation = 'softmax') %>% compile( loss = 'categorical_crossentropy', optimizer = optimizer_sgd(lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = TRUE) ) # train model %>% fit(x_train, y_train, batch_size = 32, epochs = 10) # evaluate score <- model %>% evaluate(x_test, y_test, batch_size = 32)
Epoch 1/10 5000/5000 [==============================] - 20s - loss: 1.1239 Epoch 2/10 5000/5000 [==============================] - 25s - loss: 0.2766 Epoch 3/10 5000/5000 [==============================] - 23s - loss: 0.1871 Epoch 4/10 5000/5000 [==============================] - 21s - loss: 0.1420 Epoch 5/10 5000/5000 [==============================] - 23s - loss: 0.1142 Epoch 6/10 5000/5000 [==============================] - 23s - loss: 0.1089 Epoch 7/10 5000/5000 [==============================] - 22s - loss: 0.0929 Epoch 8/10 5000/5000 [==============================] - 22s - loss: 0.0819 Epoch 9/10 5000/5000 [==============================] - 22s - loss: 0.0674 Epoch 10/10 5000/5000 [==============================] - 21s - loss: 0.0616 > > # evaluate > score <- model %>% evaluate(x_test, y_test, batch_size = 32) 992/1000 [============================>.] - ETA: 0s > score [1] 0.05340803 > pred_class <- model %>% predict(x_test, batch_size=100) > pred_label <- t(max.col(pred_class)) > table(test[,1], pred_label) pred_label 1 2 3 4 5 6 7 8 9 10 0 98 0 0 0 0 0 2 0 0 0 1 0 100 0 0 0 0 0 0 0 0 2 0 0 99 0 0 0 0 1 0 0 3 0 0 0 100 0 0 0 0 0 0 4 0 2 0 0 97 0 1 0 0 0 5 0 0 0 0 0 100 0 0 0 0 6 1 0 0 0 0 2 97 0 0 0 7 0 0 0 1 0 0 0 99 0 0 8 0 1 1 1 0 1 0 0 96 0 9 0 0 0 0 1 1 0 1 0 97 > sum(diag(table(test[,1], pred_label)))/nrow(test) [1] 0.983
ACC 0.983ということで、生TensorFlowでやってみてもほぼ同じ結果になったことが分かります。