TensorFlow Addons कॉलबैक: टाइमस्टॉपिंग

TensorFlow.org पर देखें Google Colab में चलाएं GitHub पर स्रोत देखें नोटबुक डाउनलोड करें

अवलोकन

यह नोटबुक प्रदर्शित करेगी कि TensorFlow Addons में TimeStoping Callback का उपयोग कैसे करें।

सेट अप

pip install -q -U tensorflow-addons
import tensorflow_addons as tfa

from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten

डेटा आयात और सामान्य करें

# 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
11493376/11490434 [==============================] - 0s 0us/step

सरल एमएनआईएसटी सीएनएन मॉडल बनाएं

# 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'])

सरल समय रोकना उपयोग

# initialize TimeStopping callback 
time_stopping_callback = tfa.callbacks.TimeStopping(seconds=5, verbose=1)

# 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=100,
          callbacks=[time_stopping_callback],
          validation_data=(x_test, y_test))
Epoch 1/100
938/938 [==============================] - 3s 3ms/step - loss: 0.5649 - accuracy: 0.8378 - val_loss: 0.1624 - val_accuracy: 0.9548
Epoch 2/100
938/938 [==============================] - 2s 2ms/step - loss: 0.1684 - accuracy: 0.9514 - val_loss: 0.1160 - val_accuracy: 0.9653
Timed stopping at epoch 2 after training for 0:00:05
<tensorflow.python.keras.callbacks.History at 0x7f3b947672b0>