理解张量板图像

Question

我想用keras+tensorboard。我的架构如下所示：

tbCallBack = TensorBoard(log_dir='./logs', histogram_freq=2, batch_size=32, write_graph=True, write_grads=True, write_images=True)

K.clear_session()
sess = tf.Session()
K.set_session(sess)

input_img = Input(shape=(augmented_train_data[0].shape[0], augmented_train_data[0].shape[1], 3))

x = Conv2D(8, (1, 1), padding='same', activation='relu', name="1x1_1")(input_img)
x = Conv2D(16, (3, 3), padding='same', activation='relu', name="3x3_1")(x)
x = Conv2D(32, (3, 3), padding='same', activation='relu', name="3x3_2")(x)
x = Conv2D(1, (1, 1), padding='same', activation='relu', name="1x1_2")(x)
x = Flatten()(x)
x = Dense(16, activation='relu')(x)


output = Dense(2)(x)

model = Model(inputs=input_img, outputs=output)
model.compile(optimizer='adam', loss='mean_squared_error')

#tbCallBack.set_model(model)
print(model.summary())

history = model.fit(augmented_train_data, augmented_train_label, validation_data=[augmented_validation_data, augmented_validation_label] ,epochs=20, batch_size=32, callbacks=[tbCallBack])

查看张量板图像选项卡时，它看起来像这样 不过，我不能完全解释这一点，我认为这个选项卡将显示我的卷积的权重在各个时期是如何发展的。那么，如何解释这些图像。还是我在设置张量板时做错了？

Answer 1

看起来这正是你得到的。图像的灰度显示权重。顶部的滑块可用于在 epoch 中来回移动，从而查看训练进度。