(目次はこちら)
#はじめに
多項ロジスティック回帰 [TensorFlowでDeep Learning 2]をtensorflow2.0で実現するためにはどうしたらいいのかを書く(tf.keras)。
コード
Python: 3.6.8, Tensorflow: 2.0.0a0で動作確認済み
多項ロジスティック回帰 [TensorFlowでDeep Learning 2] (mnist_softmax.py)を書き換えると、
v2/mnist_softmax.py
from helper import *
IMAGE_SIZE = 28 * 28
CATEGORY_NUM = 10
LEARNING_RATE = 0.1
EPOCHS = 30
BATCH_SIZE = 100
LOG_DIR = 'log_softmax'
EPS = 1e-10
def loss_fn(y_true, y):
y = tf.clip_by_value(y, EPS, 1.0)
return -tf.reduce_sum(y_true * tf.math.log(y), axis=1)
class LR(tf.keras.layers.Layer):
def __init__(self, units, *args, **kwargs):
super().__init__(*args, **kwargs)
self.units = units
def build(self, input_shape):
input_dim = int(input_shape[-1])
self.W = self.add_weight(
name='weight',
shape=(input_dim, self.units),
initializer=tf.keras.initializers.GlorotUniform()
)
self.b = self.add_weight(
name='bias',
shape=(self.units,),
initializer=tf.keras.initializers.Zeros()
)
self.built = True
def call(self, x):
return tf.nn.softmax(tf.matmul(x, self.W) + self.b)
if __name__ == '__main__':
(X_train, y_train), (X_test, y_test) = mnist_samples(flatten_image=True)
model = tf.keras.models.Sequential()
model.add(LR(CATEGORY_NUM, input_shape=(IMAGE_SIZE,)))
model.compile(loss=loss_fn, optimizer=tf.keras.optimizers.SGD(LEARNING_RATE), metrics=['accuracy'])
cb = [tf.keras.callbacks.TensorBoard(log_dir=LOG_DIR)]
model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=cb, validation_data=(X_test, y_test))
print(model.evaluate(X_test, y_test))
と書ける。
ロジスティック回帰との違いは、交差エントロピーの最小化となることと、
return -tf.reduce_sum(y_true * tf.math.log(y), axis=1)
sigmoid()がsoftmax()になるくらい。
return tf.nn.softmax(tf.matmul(x, self.W) + self.b)
前回と同様に、シンプルに書ける。
v2/mnist_softmax_simple.py
from helper import *
IMAGE_SIZE = 28 * 28
CATEGORY_NUM = 10
LEARNING_RATE = 0.1
EPOCHS = 30
BATCH_SIZE = 100
LOG_DIR = 'log_softmax'
if __name__ == '__main__':
(X_train, y_train), (X_test, y_test) = mnist_samples(flatten_image=True)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(CATEGORY_NUM, input_shape=(IMAGE_SIZE,), activation='softmax'))
model.compile(
loss='categorical_crossentropy',
optimizer=tf.keras.optimizers.SGD(LEARNING_RATE), metrics=['accuracy'])
cb = [tf.keras.callbacks.TensorBoard(log_dir=LOG_DIR)]
model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=cb, validation_data=(X_test, y_test))
print(model.evaluate(X_test, y_test))
めでたしめでたし
