[英]Keras training progress bar on one line with epoch number
When I use Keras to train a model with model.fit()
, I see a progress bar that looks like this:当我使用model.fit()
用model.fit()
训练模型时,我看到一个如下所示的进度条:
Epoch 1/10
8000/8000 [==========] - 55s 7ms/step - loss: 0.9318 - acc: 0.0783 - val_loss: 0.8631 - val_acc: 0.1180
Epoch 2/10
8000/8000 [==========] - 55s 7ms/step - loss: 0.6587 - acc: 0.1334 - val_loss: 0.7052 - val_acc: 0.1477
Epoch 3/10
8000/8000 [==========] - 54s 7ms/step - loss: 0.5701 - acc: 0.1526 - val_loss: 0.6445 - val_acc: 0.1632
To improve readability, I would like to have the epoch number on the same line as the progress bar, like this:为了提高可读性,我希望将纪元编号与进度条位于同一行,如下所示:
Epoch 1/10: 8000/8000 [==========] - 55s 7ms/step - loss: 0.9318 - acc: 0.0783 - val_loss: 0.8631 - val_acc: 0.1180
Epoch 2/10: 8000/8000 [==========] - 55s 7ms/step - loss: 0.6587 - acc: 0.1334 - val_loss: 0.7052 - val_acc: 0.1477
Epoch 3/10: 8000/8000 [==========] - 54s 7ms/step - loss: 0.5701 - acc: 0.1526 - val_loss: 0.6445 - val_acc: 0.1632
How can I make that change?我怎样才能做出这样的改变? I know that Keras has callbacks that can be invoked during training, but I am not familiar with how that works.我知道 Keras 有可以在训练期间调用的回调,但我不熟悉它是如何工作的。
If you want to use an alternative, you could use tqdm
(version >= 4.41.0):如果您想使用替代方案,您可以使用tqdm
(版本 >= 4.41.0):
from tqdm.keras import TqdmCallback
...
model.fit(..., verbose=0, callbacks=[TqdmCallback(verbose=2)])
This turns off keras
' progress ( verbose=0
), and uses tqdm
instead.这会关闭keras
的进度( verbose=0
),并使用tqdm
代替。 For the callback, verbose=2
means separate progressbars for epochs and batches.对于回调, verbose=2
表示 epochs 和 batches 的单独进度条。 1
means clear batch bars when done. 1
表示完成后清除批处理条。 0
means only show epochs (never show batch bars). 0
表示只显示纪元(从不显示批次条)。
Yes, you can use callbacks ( https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/Callback ).是的,您可以使用回调( https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/Callback )。 For example:例如:
import tensorflow as tf
class PrintLogs(tf.keras.callbacks.Callback):
def __init__(self, epochs):
self.epochs = epochs
def set_params(self, params):
params['epochs'] = 0
def on_epoch_begin(self, epoch, logs=None):
print('Epoch %d/%d' % (epoch + 1, self.epochs), end='')
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
epochs = 5
model.fit(x_train, y_train,
epochs=epochs,
validation_split=0.2,
verbose = 2,
callbacks=[PrintLogs(epochs)])
output:输出:
Train on 48000 samples, validate on 12000 samples
Epoch 1/5 - 10s - loss: 0.0306 - acc: 0.9901 - val_loss: 0.0837 - val_acc: 0.9786
Epoch 2/5 - 9s - loss: 0.0269 - acc: 0.9910 - val_loss: 0.0839 - val_acc: 0.9788
Epoch 3/5 - 9s - loss: 0.0253 - acc: 0.9915 - val_loss: 0.0895 - val_acc: 0.9781
Epoch 4/5 - 9s - loss: 0.0201 - acc: 0.9930 - val_loss: 0.0871 - val_acc: 0.9792
Epoch 5/5 - 9s - loss: 0.0206 - acc: 0.9931 - val_loss: 0.0917 - val_acc: 0.9793
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