理解tensorboard图像

Question

我想使用keras+tensorboard。我的架构看起来像这样：

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tensorboard图像选项卡时，它看起来如下所示

虽然我不能很好地解释这一点，但我认为这个选项卡可以显示我的卷积的权重是如何在历元上发展的。那么，如何解释这些图像。还是我在设置tensorboard时弄错了？

Answer 1

看起来这就是你所得到的。图像的灰度显示权重。顶部的滑块可用于在纪元中来回切换，从而查看训练进度。