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TensorFlow Conv Tutorial
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import tensorflow as tf | |
import time | |
# from tensorflow.python.compiler.mlcompute import mlcompute | |
# mlcompute.set_mlc_device(device_name='any') | |
mnist = tf.keras.datasets.mnist | |
(x_train, y_train), (x_test, y_test) = mnist.load_data() | |
x_train = x_train.reshape((60000, 28, 28, 1)) | |
x_test = x_test.reshape((10000, 28, 28, 1)) | |
x_train, x_test = x_train / 255.0, x_test / 255.0 | |
model = tf.keras.models.Sequential([ | |
tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)), | |
tf.keras.layers.MaxPooling2D((2,2)), | |
tf.keras.layers.Conv2D(64, (3,3), activation='relu'), | |
tf.keras.layers.MaxPooling2D((2,2)), | |
tf.keras.layers.Conv2D(64, (3,3), activation='relu'), | |
tf.keras.layers.Flatten(), | |
tf.keras.layers.Dense(64, activation='relu'), | |
tf.keras.layers.Dense(10, activation='softmax') | |
]) | |
# tf.keras.layers.Dropout(0.2), | |
model.compile(optimizer='adam', | |
loss='sparse_categorical_crossentropy', | |
metrics=['accuracy']) | |
start = time.perf_counter() | |
model.fit(x_train, y_train, epochs=5) | |
end = time.perf_counter() | |
print(f"time: {end - start:.3f}") | |
model.evaluate(x_test, y_test) |
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