mirror of
https://github.com/dragen1860/TensorFlow-2.x-Tutorials.git
synced 2021-05-12 18:32:23 +03:00
67 lines
1.8 KiB
Python
Executable File
67 lines
1.8 KiB
Python
Executable File
import tensorflow as tf
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from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
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def preprocess(x, y):
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"""
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x is a simple image, not a batch
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"""
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x = tf.cast(x, dtype=tf.float32) / 255.
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x = tf.reshape(x, [28*28])
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y = tf.cast(y, dtype=tf.int32)
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y = tf.one_hot(y, depth=10)
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return x,y
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batchsz = 128
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(x, y), (x_val, y_val) = datasets.mnist.load_data()
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print('datasets:', x.shape, y.shape, x.min(), x.max())
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db = tf.data.Dataset.from_tensor_slices((x,y))
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db = db.map(preprocess).shuffle(60000).batch(batchsz)
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ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
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ds_val = ds_val.map(preprocess).batch(batchsz)
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sample = next(iter(db))
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print(sample[0].shape, sample[1].shape)
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network = Sequential([layers.Dense(256, activation='relu'),
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layers.Dense(128, activation='relu'),
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layers.Dense(64, activation='relu'),
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layers.Dense(32, activation='relu'),
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layers.Dense(10)])
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network.build(input_shape=(None, 28*28))
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network.summary()
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network.compile(optimizer=optimizers.Adam(lr=0.01),
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loss=tf.losses.CategoricalCrossentropy(from_logits=True),
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metrics=['accuracy']
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)
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network.fit(db, epochs=3, validation_data=ds_val, validation_freq=2)
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network.evaluate(ds_val)
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network.save('model.h5')
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print('saved total model.')
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del network
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print('loaded model from file.')
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network = tf.keras.models.load_model('model.h5', compile=False)
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network.compile(optimizer=optimizers.Adam(lr=0.01),
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loss=tf.losses.CategoricalCrossentropy(from_logits=True),
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metrics=['accuracy']
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)
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x_val = tf.cast(x_val, dtype=tf.float32) / 255.
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x_val = tf.reshape(x_val, [-1, 28*28])
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y_val = tf.cast(y_val, dtype=tf.int32)
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y_val = tf.one_hot(y_val, depth=10)
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ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(128)
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network.evaluate(ds_val)
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