foscat.BkTensorflow =================== .. py:module:: foscat.BkTensorflow Classes ------- .. autoapisummary:: foscat.BkTensorflow.BkTensorflow Module Contents --------------- .. py:class:: BkTensorflow(*args, **kwargs) Bases: :py:obj:`foscat.BkBase.BackendBase` .. py:attribute:: backend .. py:attribute:: tf_function .. py:attribute:: float64 .. py:attribute:: float32 .. py:attribute:: int64 .. py:attribute:: int32 .. py:attribute:: complex64 .. py:attribute:: complex128 .. py:attribute:: gpulist .. py:attribute:: ngpu :value: 1 .. py:method:: tf_loc_function(func) .. py:method:: bk_len(S) .. py:method:: bk_SparseTensor(indice, w, dense_shape=[]) .. py:method:: bk_stack(list, axis=0) .. py:method:: bk_sparse_dense_matmul(smat, mat) .. py:method:: periodic_pad(x, pad_height, pad_width) Applies periodic ('wrap') padding to a 4D TensorFlow tensor (N, H, W, C). Args: x (tf.Tensor): Input tensor with shape (batch_size, height, width, channels). pad_height (tuple): Tuple (top, bottom) defining the vertical padding size. pad_width (tuple): Tuple (left, right) defining the horizontal padding size. Returns: tf.Tensor: Tensor with periodic padding applied. .. py:method:: binned_mean(data, cell_ids) data: Tensor of shape [..., N] (float32 or float64) cell_ids: Tensor of shape [N], int indices in [0, n_bins) Returns: mean per bin, shape [..., n_bins] .. py:method:: conv2d(x, w) Perform 2D convolution using TensorFlow. Args: x: Tensor of shape [..., Nx, Ny] – input w: Tensor of shape [O_c, wx, wy] – conv weights Returns: Tensor of shape [..., O_c, Nx, Ny] .. py:method:: conv1d(x, w) Perform 1D convolution using TensorFlow. Args: x: Tensor of shape [..., N] – input w: Tensor of shape [k] – conv weights Returns: Tensor of shape [...,N] .. py:method:: bk_threshold(x, threshold, greater=True) .. py:method:: bk_maximum(x1, x2) .. py:method:: bk_device(device_name) .. py:method:: bk_ones(shape, dtype=None) .. py:method:: bk_conv1d(x, w) .. py:method:: bk_flattenR(x) .. py:method:: bk_flatten(x) .. py:method:: bk_resize_image(x, shape) .. py:method:: bk_L1(x) .. py:method:: bk_square_comp(x) .. py:method:: bk_reduce_sum(data, axis=None) .. py:method:: bk_size(data) .. py:method:: bk_reduce_mean(data, axis=None) .. py:method:: bk_reduce_min(data, axis=None) .. py:method:: bk_random_seed(value) .. py:method:: bk_random_uniform(shape) .. py:method:: bk_reduce_std(data, axis=None) .. py:method:: bk_sqrt(data) .. py:method:: bk_abs(data) .. py:method:: bk_is_complex(data) .. py:method:: bk_distcomp(data) .. py:method:: bk_norm(data) .. py:method:: bk_square(data) .. py:method:: bk_log(data) .. py:method:: bk_matmul(a, b) .. py:method:: bk_tensor(data) .. py:method:: bk_shape_tensor(shape) .. py:method:: bk_complex(real, imag) .. py:method:: bk_exp(data) .. py:method:: bk_min(data) .. py:method:: bk_argmin(data) .. py:method:: bk_tanh(data) .. py:method:: bk_max(data) .. py:method:: bk_argmax(data) .. py:method:: bk_reshape(data, shape) .. py:method:: bk_repeat(data, nn, axis=0) .. py:method:: bk_tile(data, nn, axis=0) .. py:method:: bk_roll(data, nn, axis=0) .. py:method:: bk_expand_dims(data, axis=0) .. py:method:: bk_transpose(data, thelist) .. py:method:: bk_concat(data, axis=None) .. py:method:: bk_zeros(shape, dtype=None) .. py:method:: bk_gather(data, idx, axis=0) .. py:method:: bk_reverse(data, axis=0) .. py:method:: bk_fft(data) .. py:method:: bk_fftn(data, dim=None) .. py:method:: bk_ifftn(data, dim=None, norm=None) .. py:method:: bk_rfft(data) .. py:method:: bk_irfft(data) .. py:method:: bk_conjugate(data) .. py:method:: bk_real(data) .. py:method:: bk_imag(data) .. py:method:: bk_relu(x) .. py:method:: bk_clip_by_value(x, xmin, xmax) .. py:method:: bk_cast(x) .. py:method:: bk_variable(x) .. py:method:: bk_assign(x, y) .. py:method:: bk_constant(x) .. py:method:: bk_cos(x) .. py:method:: bk_sin(x) .. py:method:: bk_arctan2(c, s) .. py:method:: bk_empty(list) .. py:method:: to_numpy(x)