experiments/STT-tensorflow
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remove conv2d() layers

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Joseph-Rance 2020-01-21 20:22:13 +00:00 committed by GitHub
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1 changed files with 11 additions and 14 deletions
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tensorflow/python/keras/layers/pooling.py
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@ -373,24 +373,21 @@ class MaxPooling2D(Pooling2D):
Usage Example:
>>> input_image = tf.constant([[[[1.], [1.], [2.], [4.], [2.], [4.], [2.]],
... [[2.], [2.], [3.], [2.], [2.], [1.], [2.]],
... [[4.], [1.], [1.], [1.], [1.], [2.], [2.]],
... [[2.], [2.], [1.], [4.], [2.], [3.], [4.]],
... [[1.], [4.], [1.], [1.], [2.], [3.], [2.]],
... [[1.], [4.], [2.], [3.], [1.], [2.], [3.]],
... [[3.], [4.], [1.], [2.], [3.], [1.], [4.]]]])
>>> input_image = tf.constant([[[[1.], [1.], [2.], [4.]],
... [[2.], [2.], [3.], [2.]],
... [[4.], [1.], [1.], [1.]],
... [[2.], [2.], [1.], [4.]]]])
>>> output = tf.constant([[[[1], [0]],
... [[0], [1]]]])
>>> model = tf.keras.models.Sequential()
>>> model.add(tf.keras.layers.Conv2D(1, kernel_size=(3, 3),
... input_shape=(7,7,1)))
>>> model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
>>> model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2),
... input_shape=(4,4,1)))
>>> model.compile('adam', 'mean_squared_error')
>>> model.fit(input_image, output, steps_per_epoch=1,
... shuffle=False, verbose=0)
>>> model.predict(input_image, steps=1).shape
(1, 2, 2, 1)
>>> model.predict(input_image, steps=1)
array([[[[2.],
[4.]],
[[4.],
[4.]]]], dtype=float32)
For example, for stride=(1,1) and padding="same":