def call(self, inputs):
inputs = ops.convert_to_tensor_v2_with_dispatch(inputs,
dtype=self.dtype)
inputs = math_ops.cast(inputs, dtypes.float32)
kernel = (1.0 / self.kernel_scale) * self.unscaled_kernel
outputs = gen_math_ops.MatMul(a=inputs, b=kernel)
outputs = nn.bias_add(outputs, self.bias)
return gen_math_ops.cos(outputs)
def call(self, inputs):
if inputs.dtype.base_dtype != self._compute_dtype_object.base_dtype:
inputs = math_ops.cast(inputs, dtype=self._compute_dtype_object)
rank = inputs.shape.rank
if rank == 2 or rank is None:
# We use embedding_lookup_sparse as a more efficient matmul operation for
# large sparse input tensors. The op will result in a sparse gradient, as
# opposed to sparse_ops.sparse_tensor_dense_matmul which results in dense
# gradients. This can lead to sigfinicant speedups, see b/171762937.
if isinstance(inputs, sparse_tensor.SparseTensor):
# We need to fill empty rows, as the op assumes at least one id per row.
inputs, _ = sparse_ops.sparse_fill_empty_rows(inputs, 0)
# We need to do some munging of our input to use the embedding lookup as
# a matrix multiply. We split our input matrix into separate ids and
# weights tensors. The values of the ids tensor should be the column
# indices of our input matrix and the values of the weights tensor
# can continue to the actual matrix weights.
# The column arrangement of ids and weights
# will be summed over and does not matter. See the documentation for
# sparse_ops.sparse_tensor_dense_matmul a more detailed explanation
# of the inputs to both ops.
ids = sparse_tensor.SparseTensor(
indices=inputs.indices,
values=inputs.indices[:, 1],
dense_shape=inputs.dense_shape)
weights = inputs
outputs = embedding_ops.embedding_lookup_sparse_v2(
self.kernel * self.window, ids, weights, combiner='sum')
else:
outputs = gen_math_ops.MatMul(a=inputs,
b=self.kernel * self.window)
# Broadcast kernel to inputs.
else:
outputs = standard_ops.tensordot(inputs, self.kernel * self.window,
[[rank - 1], [0]])
# Reshape the output back to the original ndim of the input.
if not context.executing_eagerly():
shape = inputs.shape.as_list()
output_shape = shape[:-1] + [self.kernel.shape[-1]]
outputs.set_shape(output_shape)
if self.use_bias:
outputs = nn_ops.bias_add(outputs, self.bias)
if self.activation is not None:
outputs = self.activation(outputs)
return outputs
def dense(inputs, kernel, bias=None, activation=None, dtype=None):
"""Densely connected NN layer op.
Args:
inputs: `tf.Tensor` or `tf.SparseTensor`. Inputs to operation.
kernel: `tf.Variable`. Matrix kernel.
bias: (Optional) `tf.Variable`. Bias to add to outputs.
activation: (Optional) 1-argument callable. Activation function to apply to
outputs.
dtype: (Optional) `tf.DType`. Dtype to cast `inputs` to.
Returns:
`tf.Tensor`. Output of dense connection.
"""
if dtype:
if inputs.dtype.base_dtype != dtype.base_dtype:
inputs = math_ops.cast(inputs, dtype=dtype)
rank = inputs.shape.rank
if rank == 2 or rank is None:
if isinstance(inputs, sparse_tensor.SparseTensor):
outputs = sparse_ops.sparse_tensor_dense_matmul(inputs, kernel)
else:
outputs = gen_math_ops.MatMul(a=inputs, b=kernel)
# Broadcast kernel to inputs.
else:
outputs = standard_ops.tensordot(inputs, kernel, [[rank - 1], [0]])
# Reshape the output back to the original ndim of the input.
if not context.executing_eagerly():
shape = inputs.shape.as_list()
output_shape = shape[:-1] + [kernel.shape[-1]]
outputs.set_shape(output_shape)
if bias is not None:
outputs = nn_ops.bias_add(outputs, bias)
if activation is not None:
outputs = activation(outputs)
return outputs
def dense(inputs, kernel, bias=None, activation=None, dtype=None):
"""Densely connected NN layer op.
Args:
inputs: `tf.Tensor` or `tf.SparseTensor`. Inputs to operation.
kernel: `tf.Variable`. Matrix kernel.
bias: (Optional) `tf.Variable`. Bias to add to outputs.
activation: (Optional) 1-argument callable. Activation function to apply to
outputs.
dtype: (Optional) `tf.DType`. Dtype to cast `inputs` to.
Returns:
`tf.Tensor`. Output of dense connection.
"""
if dtype:
if inputs.dtype.base_dtype != dtype.base_dtype:
inputs = math_ops.cast(inputs, dtype=dtype)
rank = inputs.shape.rank
if rank == 2 or rank is None:
# We use embedding_lookup_sparse as a more efficient matmul operation for
# large sparse input tensors. The op will result in a sparse gradient, as
# opposed to sparse_ops.sparse_tensor_dense_matmul which results in dense
# gradients. This can lead to sigfinicant speedups, see b/171762937.
if isinstance(inputs, sparse_tensor.SparseTensor):
# We need to fill empty rows, as the op assumes at least one id per row.
inputs, _ = sparse_ops.sparse_fill_empty_rows(inputs, 0)
# We need to do some munging of our input to use the embedding lookup as a
# matrix multiply. We split our input matrix into separate ids and weights
# tensors. The values of the ids tensor should be the column indices of
# our input matrix and the values of the weights tensor can continue to
# the actual matrix weights. The column arrangement of ids and weights
# will be summed over and does not matter. See the documentation for
# sparse_ops.sparse_tensor_dense_matmul a more detailed explanation of the
# inputs to both ops.
ids = sparse_tensor.SparseTensor(indices=inputs.indices,
values=inputs.indices[:, 1],
dense_shape=inputs.dense_shape)
weights = inputs
outputs = embedding_ops.embedding_lookup_sparse_v2(kernel,
ids,
weights,
combiner="sum")
else:
outputs = gen_math_ops.MatMul(a=inputs, b=kernel)
# Broadcast kernel to inputs.
else:
outputs = standard_ops.tensordot(inputs, kernel, [[rank - 1], [0]])
# Reshape the output back to the original ndim of the input.
if not context.executing_eagerly():
shape = inputs.shape.as_list()
output_shape = shape[:-1] + [kernel.shape[-1]]
outputs.set_shape(output_shape)
if bias is not None:
outputs = nn_ops.bias_add(outputs, bias)
if activation is not None:
outputs = activation(outputs)
return outputs