TensorFlow Addons Callbacks: TQDM Progress Bar

pipinstall-Utensorflow-addons

!pip install -q "tqdm>=4.36.1"
importtensorflowastf
importtensorflow_addonsastfa
fromtensorflow.keras.datasetsimport mnist
fromtensorflow.keras.modelsimport Sequential
fromtensorflow.keras.layersimport Dense, Dropout, Flatten

importtqdm
# quietly deep-reload tqdm
importsys
fromIPython.libimport deepreload 
stdout = sys.stdout
sys.stdout = open('junk','w')
deepreload.reload(tqdm)
sys.stdout = stdout
tqdm.__version__

'4.64.0'

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# normalize data
x_train, x_test = x_train / 255.0, x_test / 255.0

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11490434/11490434 [==============================] - 0s 0us/step

# build the model using the Sequential API
model = Sequential()
model.add(Flatten(input_shape=(28, 28)))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam',
 loss = 'sparse_categorical_crossentropy',
 metrics=['accuracy'])

# initialize tqdm callback with default parameters
tqdm_callback = tfa.callbacks.TQDMProgressBar()
# train the model with tqdm_callback
# make sure to set verbose = 0 to disable
# the default progress bar.
model.fit(x_train, y_train,
 batch_size=64,
 epochs=10,
 verbose=0,
 callbacks=[tqdm_callback],
 validation_data=(x_test, y_test))

Training: 0%| 0/10 ETA: ?s, ?epochs/s
Epoch 1/10
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Epoch 2/10
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Epoch 3/10
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Epoch 4/10
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Epoch 5/10
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Epoch 6/10
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Epoch 7/10
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Epoch 8/10
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Epoch 9/10
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Epoch 10/10
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<keras.callbacks.History at 0x7f49161ea7f0>

# TQDMProgressBar() also works with evaluate()
model.evaluate(x_test, y_test, batch_size=64, callbacks=[tqdm_callback], verbose=0)

0/157 ETA: ?s - Evaluating
[0.07728561758995056, 0.9760000109672546]