tf.nn.ctc

Depthwise 2-D convolution.

tf.nn.depthwise_conv2d(
    input, filter, strides, padding, data_format=None, dilations=None, name=None
)

Given a 4D input tensor ('NHWC' or 'NCHW' data formats) and a filter tensor of shape [filter_height, filter_width, in_channels, channel_multiplier] containing in_channels convolutional filters of depth 1, depthwise_conv2d applies a different filter to each input channel (expanding from 1 channel to channel_multiplier channels for each), then concatenates the results together. The output has in_channels * channel_multiplier channels.

In detail, with the default NHWC format,

output[b, i, j, k * channel_multiplier + q] = sum_{di, dj}
     filter[di, dj, k, q] * input[b, strides[1] * i + rate[0] * di,
                                     strides[2] * j + rate[1] * dj, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertical strides, strides = [1, stride, stride, 1]. If any value in rate is greater than 1, we perform atrous depthwise convolution, in which case all values in the strides tensor must be equal to 1.

Usage Example:

x = np.array([
    [1., 2.],
    [3., 4.],
    [5., 6.]
], dtype=np.float32).reshape((1, 3, 2, 1))
kernel = np.array([
    [1., 2.],
    [3., 4]
], dtype=np.float32).reshape((2, 1, 1, 2))
tf.nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1],
                       padding='VALID').numpy()
  array([[[[10., 14.],
           [14., 20.]],
          [[18., 26.],
           [22., 32.]]]], dtype=float32)

tf.nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1],
                       padding=[[0, 0], [1, 0], [1, 0], [0, 0]]).numpy()
  array([[[[ 0.,  0.],
           [ 3.,  4.],
           [ 6.,  8.]],
          [[ 0.,  0.],
           [10., 14.],
           [14., 20.]],
          [[ 0.,  0.],
           [18., 26.],
           [22., 32.]]]], dtype=float32)

Args
`input`	4-D with shape according to `data_format`.
`filter`	4-D with shape `[filter_height, filter_width, in_channels, channel_multiplier]`.
`strides`	1-D of size 4. The stride of the sliding window for each dimension of `input`.
`padding`	Controls how to pad the image before applying the convolution. Can be the string `"SAME"` or `"VALID"` indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]`.
`data_format`	The data format for input. Either "NHWC" (default) or "NCHW".
`dilations`	1-D of size 2. The dilation rate in which we sample input values across the `height` and `width` dimensions in atrous convolution. If it is greater than 1, then all values of strides must be 1.
`name`	A name for this operation (optional).

Returns
A 4-D `Tensor` with shape according to `data_format`. E.g., for "NHWC" format, shape is `[batch, out_height, out_width, in_channels * channel_multiplier].`

TensorFlow

tf.nn / ctc_loss

Usage Example:

Args

Returns

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tf.nn / ctc_loss

Usage Example:

Args

Returns