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@ -23,6 +23,7 @@ from layer_function_generator import autodoc, templatedoc
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from tensor import concat
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import utils
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import random
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from .. import unique_name
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__all__ = [
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'fc',
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@ -4266,14 +4267,18 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
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say :attr:`actual_shape` has a higher priority
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than :attr:`shape`.
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act (str): The non-linear activation to be applied to output variable.
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inplace(bool): If this flag is set true, a new output tensor is created
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whose data is copied from input x, otherwise the output
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shares data with input without copying.
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inplace(bool): If this flag is set true, the output
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shares data with input without copying, otherwise
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a new output tensor is created
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whose data is copied from input x.
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name (str): The name of this layer. It is optional.
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Returns:
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Variable: The output tensor.
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Raises:
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TypeError: if actual_shape is neither Variable nor None.
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Examples:
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.. code-block:: python
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@ -4285,6 +4290,11 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
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if not (isinstance(shape, list) or isinstance(shape, tuple)):
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raise ValueError("Input shape must be a python lsit or tuple.")
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inputs = {"X": x}
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if isinstance(actual_shape, Variable):
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inputs["Shape"] = actual_shape
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elif actual_shape is not None:
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raise TypeError("actual_shape should either be Variable or None")
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# Validate the shape
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unk_dim_idx = -1
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@ -4305,9 +4315,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
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reshaped = helper.create_tmp_variable(dtype=x.dtype)
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helper.append_op(
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type="reshape",
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inputs={"X": x,
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"Shape": actual_shape}
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if isinstance(actual_shape, Variable) else {"X": x},
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inputs=inputs,
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attrs={"shape": shape,
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"inplace": inplace},
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outputs={"Out": reshaped})
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@ -4889,47 +4897,39 @@ def random_crop(x, shape, seed=None):
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>>> cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
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"""
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helper = LayerHelper("random_crop", **locals())
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dtype = helper.input_dtype()
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dtype = x.dtype
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out = helper.create_tmp_variable(dtype)
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if seed is None:
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seed = random.randint(-65536, 65535)
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op_attrs = {"shape": shape}
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if isinstance(seed, int):
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seed_value = seed
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seed = helper.create_tmp_variable(dtype="int64")
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helper.append_op(
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type="fill_constant",
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inputs={},
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outputs={"Out": seed},
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attrs={
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"dtype": seed.dtype,
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"shape": [1],
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"value": float(seed_value),
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"force_cpu": True
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})
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op_attrs["startup_seed"] = seed
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seed = helper.create_variable(
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name=unique_name.generate("random_crop_seed"),
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dtype="int64",
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persistable=True)
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elif not isinstance(seed, Variable):
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raise ValueError("'seed' must be a Variable or an int.")
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seed_out = helper.create_tmp_variable(dtype="int64")
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helper.append_op(
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type="random_crop",
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inputs={"X": x,
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"Seed": seed},
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outputs={"Out": out,
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"SeedOut": seed_out},
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attrs={"shape": shape})
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"SeedOut": seed},
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attrs=op_attrs)
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return out
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def log(input):
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def log(x):
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"""
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Calculates the natural log of the given input tensor, element-wise.
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.. math::
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Out = \\ln(input)
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Out = \\ln(x)
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Args:
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input (Variable): Input tensor.
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x (Variable): Input tensor.
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Returns:
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Variable: The natural log of the input tensor computed element-wise.
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@ -4938,7 +4938,7 @@ def log(input):
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.. code-block:: python
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output = fluid.layers.log(input)
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output = fluid.layers.log(x)
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"""
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helper = LayerHelper('log', **locals())
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dtype = helper.input_dtype(input_param_name='x')
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@ -4947,18 +4947,18 @@ def log(input):
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return out
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def relu(input):
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def relu(x):
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"""
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Relu takes one input data (Tensor) and produces one output data (Tensor)
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where the rectified linear function, y = max(0, input), is applied to
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where the rectified linear function, y = max(0, x), is applied to
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the tensor elementwise.
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.. math::
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Out = \\max(0, input)
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Out = \\max(0, x)
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Args:
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input (Variable): The input tensor.
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x (Variable): The input tensor.
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Returns:
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Variable: The output tensor with the same shape as input.
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@ -4967,7 +4967,7 @@ def relu(input):
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.. code-block:: python
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output = fluid.layers.relu(input)
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output = fluid.layers.relu(x)
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"""
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helper = LayerHelper('relu', **locals())
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dtype = helper.input_dtype(input_param_name='x')
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Yancey1989
7 years ago