keras：在progress bar 輸出學習率的變化

It can be achieved via a custom metric. Take the learning rate as an example:

def get_lr_metric(optimizer):
    def lr(y_true, y_pred):
        return optimizer.lr
    return lr

x = Input((50,))
out = Dense(1, activation='sigmoid')(x)
model = Model(x, out)

optimizer = Adam(lr=0.001)
lr_metric = get_lr_metric(optimizer)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['acc', lr_metric])

# reducing the learning rate by half every 2 epochs
cbks = [LearningRateScheduler(lambda epoch: 0.001 * 0.5 ** (epoch // 2)),
        TensorBoard(write_graph=False)]
X = np.random.rand(1000, 50)
Y = np.random.randint(2, size=1000)
model.fit(X, Y, epochs=10, callbacks=cbks)
           

The LR will be printed in the progress bar:

Epoch 1/10
1000/1000 [==============================] - 0s 103us/step - loss: 0.8228 - acc: 0.4960 - lr: 0.0010
Epoch 2/10
1000/1000 [==============================] - 0s 61us/step - loss: 0.7305 - acc: 0.4970 - lr: 0.0010
Epoch 3/10
1000/1000 [==============================] - 0s 62us/step - loss: 0.7145 - acc: 0.4730 - lr: 5.0000e-04
Epoch 4/10
1000/1000 [==============================] - 0s 58us/step - loss: 0.7129 - acc: 0.4800 - lr: 5.0000e-04
Epoch 5/10
1000/1000 [==============================] - 0s 58us/step - loss: 0.7124 - acc: 0.4810 - lr: 2.5000e-04
Epoch 6/10
1000/1000 [==============================] - 0s 63us/step - loss: 0.7123 - acc: 0.4790 - lr: 2.5000e-04
Epoch 7/10
1000/1000 [==============================] - 0s 61us/step - loss: 0.7119 - acc: 0.4840 - lr: 1.2500e-04
Epoch 8/10
1000/1000 [==============================] - 0s 61us/step - loss: 0.7117 - acc: 0.4880 - lr: 1.2500e-04
Epoch 9/10
1000/1000 [==============================] - 0s 59us/step - loss: 0.7116 - acc: 0.4880 - lr: 6.2500e-05
Epoch 10/10
1000/1000 [==============================] - 0s 63us/step - loss: 0.7115 - acc: 0.4880 - lr: 6.2500e-05
           

Then, you can visualize the LR curve in TensorBoard.