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TensorFlow 1 version
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View source on GitHub
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Max pooling operation for 1D temporal data.
tf.keras.layers.MaxPool1D(
pool_size=2, strides=None, padding='valid',
data_format='channels_last', **kwargs
)
Used in the notebooks
| Used in the tutorials |
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Downsamples the input representation by taking the maximum value over the
window defined by pool_size. The window is shifted by strides. The
resulting output when using "valid" padding option has a shape of:
output_shape = (input_shape - pool_size + 1) / strides)
The resulting output shape when using the "same" padding option is:
output_shape = input_shape / strides
For example, for strides=1 and padding="valid":
x = tf.constant([1., 2., 3., 4., 5.])x = tf.reshape(x, [1, 5, 1])max_pool_1d = tf.keras.layers.MaxPooling1D(pool_size=2,strides=1, padding='valid')max_pool_1d(x)<tf.Tensor: shape=(1, 4, 1), dtype=float32, numpy=array([[[2.],[3.],[4.],[5.]]], dtype=float32)>
For example, for strides=2 and padding="valid":
x = tf.constant([1., 2., 3., 4., 5.])x = tf.reshape(x, [1, 5, 1])max_pool_1d = tf.keras.layers.MaxPooling1D(pool_size=2,strides=2, padding='valid')max_pool_1d(x)<tf.Tensor: shape=(1, 2, 1), dtype=float32, numpy=array([[[2.],[4.]]], dtype=float32)>
For example, for strides=1 and padding="same":
x = tf.constant([1., 2., 3., 4., 5.])x = tf.reshape(x, [1, 5, 1])max_pool_1d = tf.keras.layers.MaxPooling1D(pool_size=2,strides=1, padding='same')max_pool_1d(x)<tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=array([[[2.],[3.],[4.],[5.],[5.]]], dtype=float32)>
Arguments | |
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pool_size
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Integer, size of the max pooling window. |
strides
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Integer, or None. Specifies how much the pooling window moves
for each pooling step.
If None, it will default to pool_size.
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padding
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One of "valid" or "same" (case-insensitive).
"valid" means no padding. "same" results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
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data_format
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A string,
one of channels_last (default) or channels_first.
The ordering of the dimensions in the inputs.
channels_last corresponds to inputs with shape
(batch, steps, features) while channels_first
corresponds to inputs with shape
(batch, features, steps).
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Input shape:
- If
data_format='channels_last': 3D tensor with shape(batch_size, steps, features). - If
data_format='channels_first': 3D tensor with shape(batch_size, features, steps).
Output shape:
- If
data_format='channels_last': 3D tensor with shape(batch_size, downsampled_steps, features). - If
data_format='channels_first': 3D tensor with shape(batch_size, features, downsampled_steps).