import tensorflow as tf

from tensorflow import keras
input_shape = (2, 3, 4)
x = tf.random.normal(input_shape)
print(x)

y=keras.layers.GlobalMaxPool1D()(x)
print("*"*20)

print(y)
'''
  """Global average pooling operation for temporal data.

  Examples:

  >>> input_shape = (2, 3, 4)
  >>> x = tf.random.normal(input_shape)
  >>> y = tf.keras.layers.GlobalAveragePooling1D()(x)
  >>> print(y.shape)
  (2, 4)

  Arguments:
    data_format: 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)`.

  Call arguments:
    inputs: A 3D tensor.
    mask: Binary tensor of shape `(batch_size, steps)` indicating whether
      a given step should be masked (excluded from the average).

  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:
    2D tensor with shape `(batch_size, features)`.
  """
'''

print("--"*20)

input_shape = (2, 3, 4)
x = tf.random.normal(input_shape)
print(x)

y=keras.layers.MaxPool1D(pool_size=2,strides=1)(x)  # strides 不指定 默認等于 pool_size
print("*"*20)

print(y)

x = tf.constant([[1., 2., 3.], [4., 5., 6.]])
???????x = tf.reshape(x, [2, 3, 1])
max_pool_1d=tf.keras.layers.GlobalMaxPooling1D()
max_pool_1d(x)