op_average_pool: Average pooling operation.

In rstudio/keras: R Interface to 'Keras'

Average pooling operation.

Description

Average pooling operation.

Usage

op_average_pool(
  inputs,
  pool_size,
  strides = NULL,
  padding = "valid",
  data_format = NULL
)

Arguments

inputs	Tensor of rank N+2. inputs has shape ⁠(batch_size,) + inputs_spatial_shape + (num_channels,)⁠ if data_format = "channels_last", or ⁠(batch_size, num_channels) + inputs_spatial_shape⁠ if data_format = "channels_first". Pooling happens over the spatial dimensions only.
pool_size	int or tuple/list of integers of size len(inputs_spatial_shape), specifying the size of the pooling window for each spatial dimension of the input tensor. If pool_size is int, then every spatial dimension shares the same pool_size.
strides	int or tuple/list of integers of size len(inputs_spatial_shape). The stride of the sliding window for each spatial dimension of the input tensor. If strides is int, then every spatial dimension shares the same strides.
padding	string, either "valid" or "same". "valid" means no padding is applied, and "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 when strides = 1.
data_format	A string, either "channels_last" or "channels_first". data_format determines the ordering of the dimensions in the inputs. If data_format = "channels_last", inputs is of shape ⁠(batch_size, ..., channels)⁠ while if data_format = "channels_first", inputs is of shape ⁠(batch_size, channels, ...)⁠.

Value

A tensor of rank N+2, the result of the average pooling operation.

See Also

• https://keras.io/api/ops/nn#averagepool-function

Other nn ops:
op_batch_normalization()
op_binary_crossentropy()
op_categorical_crossentropy()
op_conv()
op_conv_transpose()
op_ctc_loss()
op_depthwise_conv()
op_elu()
op_gelu()
op_hard_sigmoid()
op_hard_silu()
op_leaky_relu()
op_log_sigmoid()
op_log_softmax()
op_max_pool()
op_moments()
op_multi_hot()
op_normalize()
op_one_hot()
op_relu()
op_relu6()
op_selu()
op_separable_conv()
op_sigmoid()
op_silu()
op_softmax()
op_softplus()
op_softsign()
op_sparse_categorical_crossentropy()

Other ops:
op_abs()
op_add()
op_all()
op_any()
op_append()
op_arange()
op_arccos()
op_arccosh()
op_arcsin()
op_arcsinh()
op_arctan()
op_arctan2()
op_arctanh()
op_argmax()
op_argmin()
op_argsort()
op_array()
op_average()
op_batch_normalization()
op_binary_crossentropy()
op_bincount()
op_broadcast_to()
op_cast()
op_categorical_crossentropy()
op_ceil()
op_cholesky()
op_clip()
op_concatenate()
op_cond()
op_conj()
op_conv()
op_conv_transpose()
op_convert_to_numpy()
op_convert_to_tensor()
op_copy()
op_correlate()
op_cos()
op_cosh()
op_count_nonzero()
op_cross()
op_ctc_decode()
op_ctc_loss()
op_cumprod()
op_cumsum()
op_custom_gradient()
op_depthwise_conv()
op_det()
op_diag()
op_diagonal()
op_diff()
op_digitize()
op_divide()
op_divide_no_nan()
op_dot()
op_eig()
op_eigh()
op_einsum()
op_elu()
op_empty()
op_equal()
op_erf()
op_erfinv()
op_exp()
op_expand_dims()
op_expm1()
op_extract_sequences()
op_eye()
op_fft()
op_fft2()
op_flip()
op_floor()
op_floor_divide()
op_fori_loop()
op_full()
op_full_like()
op_gelu()
op_get_item()
op_greater()
op_greater_equal()
op_hard_sigmoid()
op_hard_silu()
op_hstack()
op_identity()
op_imag()
op_image_affine_transform()
op_image_crop()
op_image_extract_patches()
op_image_map_coordinates()
op_image_pad()
op_image_resize()
op_image_rgb_to_grayscale()
op_in_top_k()
op_inv()
op_irfft()
op_is_tensor()
op_isclose()
op_isfinite()
op_isinf()
op_isnan()
op_istft()
op_leaky_relu()
op_less()
op_less_equal()
op_linspace()
op_log()
op_log10()
op_log1p()
op_log2()
op_log_sigmoid()
op_log_softmax()
op_logaddexp()
op_logical_and()
op_logical_not()
op_logical_or()
op_logical_xor()
op_logspace()
op_logsumexp()
op_lu_factor()
op_matmul()
op_max()
op_max_pool()
op_maximum()
op_mean()
op_median()
op_meshgrid()
op_min()
op_minimum()
op_mod()
op_moments()
op_moveaxis()
op_multi_hot()
op_multiply()
op_nan_to_num()
op_ndim()
op_negative()
op_nonzero()
op_norm()
op_normalize()
op_not_equal()
op_one_hot()
op_ones()
op_ones_like()
op_outer()
op_pad()
op_power()
op_prod()
op_qr()
op_quantile()
op_ravel()
op_real()
op_reciprocal()
op_relu()
op_relu6()
op_repeat()
op_reshape()
op_rfft()
op_roll()
op_round()
op_rsqrt()
op_scatter()
op_scatter_update()
op_segment_max()
op_segment_sum()
op_select()
op_selu()
op_separable_conv()
op_shape()
op_sigmoid()
op_sign()
op_silu()
op_sin()
op_sinh()
op_size()
op_slice()
op_slice_update()
op_softmax()
op_softplus()
op_softsign()
op_solve()
op_solve_triangular()
op_sort()
op_sparse_categorical_crossentropy()
op_split()
op_sqrt()
op_square()
op_squeeze()
op_stack()
op_std()
op_stft()
op_stop_gradient()
op_subtract()
op_sum()
op_svd()
op_swapaxes()
op_take()
op_take_along_axis()
op_tan()
op_tanh()
op_tensordot()
op_tile()
op_top_k()
op_trace()
op_transpose()
op_tri()
op_tril()
op_triu()
op_unstack()
op_var()
op_vdot()
op_vectorize()
op_vectorized_map()
op_vstack()
op_where()
op_while_loop()
op_zeros()
op_zeros_like()

rstudio/keras documentation built on April 27, 2024, 10:11 p.m.