28 lines
809 B
Python
28 lines
809 B
Python
import time
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import os
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import numpy as np
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os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
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import keras
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import keras.applications as kapp
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from keras.datasets import cifar10
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(x_train, y_train_cats), (x_test, y_test_cats) = cifar10.load_data()
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batch_size = 8
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x_train = x_train[:batch_size]
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x_train = np.repeat(np.repeat(x_train, 7, axis=1), 7, axis=2)
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model = kapp.VGG19()
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model.compile(optimizer='sgd', loss='categorical_crossentropy',
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metrics=['accuracy'])
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print("Running initial batch (compiling tile program)")
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y = model.predict(x=x_train, batch_size=batch_size)
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# Now start the clock and run 10 batches
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print("Timing inference...")
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start = time.time()
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for i in range(10):
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y = model.predict(x=x_train, batch_size=batch_size)
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print("Ran in {} seconds".format(time.time() - start)) |