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September 4, 2020 09:23
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############################# | |
# tensorflow2のCNNの実装例3(もっとも玄人ぽい) | |
# Subclassing APIを用いる場合 | |
############################# | |
import tensorflow as tf | |
import pdb | |
#---------------------------- | |
# データの作成 | |
# 画像サイズ(高さ,幅,チャネル数) | |
H, W, C = 28, 28, 1 | |
# MNISTデータの読み込み | |
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() | |
# 画像の正規化 | |
x_train = x_train.astype('float32') / 255 | |
x_test = x_test.astype('float32') / 255 | |
# (データ数,高さ,幅,チャネル数)にrehspae | |
x_train = x_train.reshape(x_train.shape[0], H, W, C) | |
x_test = x_test.reshape(x_test.shape[0], H, W, C) | |
#---------------------------- | |
#---------------------------- | |
# Subclassingを用いたネットワークの定義 | |
# Layerクラスを継承して独自のconvolution用のレイヤークラスを作成 | |
class myConv(tf.keras.layers.Layer): | |
def __init__(self,chn=32, conv_kernel=(3,3), pool_kernel=(2,2), isPool=True): | |
super(myConv, self).__init__() | |
self.isPool = isPool | |
self.conv = tf.keras.layers.Conv2D(chn, conv_kernel) | |
self.batchnorm = tf.keras.layers.BatchNormalization() | |
self.relu = tf.keras.layers.ReLU() | |
self.pool = tf.keras.layers.MaxPool2D(pool_kernel) | |
def call(self, x): | |
x = self.conv(x) | |
x = self.batchnorm(x) | |
x = self.relu(x) | |
if self.isPool: | |
x = self.pool(x) | |
return x | |
# Layerクラスを継承して独自のFC用のレイヤークラスを作成 | |
class myFC(tf.keras.layers.Layer): | |
def __init__(self, hidden_chn=64, out_chn=10): | |
super(myFC, self).__init__() | |
self.flatten = tf.keras.layers.Flatten() | |
self.fc1 = tf.keras.layers.Dense(hidden_chn, activation='relu') | |
self.fc2 = tf.keras.layers.Dense(out_chn, activation='softmax') | |
def call(self, x): | |
x = self.flatten(x) | |
x = self.fc1(x) | |
x = self.fc2(x) | |
return x, self.fc2.weights | |
# Modelクラスを継承し,独自のlayerクラス(myConvとmyFC)を用いてネットワークを定義する | |
# 独自のモデルクラスを作成 | |
class myModel(tf.keras.Model): | |
def __init__(self): | |
super(myModel, self).__init__() | |
self.conv1 = myConv(chn=32, conv_kernel=(3,3), pool_kernel=(2,2)) | |
self.conv2 = myConv(chn=64, conv_kernel=(3,3), pool_kernel=(2,2)) | |
self.conv3 = myConv(chn=64, conv_kernel=(3,3), isPool=False) | |
self.fc = myFC(hidden_chn=64, out_chn=10) | |
def call(self, x): | |
x = self.conv1(x) | |
x = self.conv2(x) | |
x = self.conv3(x) | |
return self.fc(x) | |
# モデルの設定 | |
model = myModel() | |
#---------------------------- | |
#---------------------------- | |
# 学習方法の設定 | |
# 学習用と評価用の関数train_stepとtest_stepを定義 | |
# @tf.functionを用いることにより,予測と損失をtensorグラフに繋げることができる | |
#損失関数 | |
loss_object = tf.keras.losses.SparseCategoricalCrossentropy() | |
#最適化関数 | |
optimizer = tf.keras.optimizers.Adam() | |
# 評価指標 | |
train_loss = tf.keras.metrics.Mean(name='train_loss') | |
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy') | |
test_loss = tf.keras.metrics.Mean(name='test_loss') | |
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy') | |
@tf.function | |
def train_step(x, t): | |
with tf.GradientTape() as tape: | |
# 予測 | |
pred, weights = model(x, training=True) | |
# 損失 | |
loss = loss_object(t, pred) + tf.norm(weights[0],axis=0) | |
# 勾配を用いた学習 | |
gradients = tape.gradient(loss, model.trainable_variables) | |
optimizer.apply_gradients(zip(gradients, model.trainable_variables)) | |
# 評価 | |
train_loss(loss) | |
train_accuracy(t, pred) | |
@tf.function | |
def test_step(x, t): | |
# 予測 | |
test_pred, test_weights = model(x) | |
# 損失 | |
t_loss = loss_object(t, test_pred) + tf.norm(test_weights[0],axis=0) | |
# 評価 | |
test_loss(t_loss) | |
test_accuracy(t, test_pred) | |
#---------------------------- | |
#---------------------------- | |
# 学習 | |
# ミニバッチの作成 | |
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32) | |
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32) | |
for epoch in range(5): | |
for images, labels in train_ds: | |
train_step(images, labels) #学習 | |
for test_images, test_labels in test_ds: | |
test_step(test_images, test_labels) #評価 | |
print(f"Epoch {epoch + 1}, Loss: {train_loss.result()}, Accuracy: {train_accuracy.result() * 100}") | |
print(f"\ttest-Loss: {test_loss.result()}, test-Accuracy{test_accuracy.result()*100}") | |
#---------------------------- |
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