HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

text stringlengths 0 4.99k
Returns whether x is a Keras tensor.

A "Keras tensor" is a tensor that was returned by a Keras layer, (Layer class) or by Input.

Arguments

x: A candidate tensor.
Returns

A boolean: Whether the argument is a Keras tensor.
Raises

ValueError: In case x is not a symbolic tensor.
Examples


>>> np_var = np.array([1, 2])
>>> # A numpy array is not a symbolic tensor.
>>> tf.keras.backend.is_keras_tensor(np_var)
Traceback (most recent call last):
...
ValueError: Unexpectedly found an instance of type `<class 'numpy.ndarray'>`.
Expected a symbolic tensor instance.
>>> keras_var = tf.keras.backend.variable(np_var)
>>> # A variable created with the keras backend is not a Keras tensor.
>>> tf.keras.backend.is_keras_tensor(keras_var)
False
>>> keras_placeholder = tf.keras.backend.placeholder(shape=(2, 4, 5))
>>> # A placeholder is a Keras tensor.
>>> tf.keras.backend.is_keras_tensor(keras_placeholder)
True
>>> keras_input = tf.keras.layers.Input([10])
>>> # An Input is a Keras tensor.
>>> tf.keras.backend.is_keras_tensor(keras_input)
True
>>> keras_layer_output = tf.keras.layers.Dense(10)(keras_input)
>>> # Any Keras layer output is a Keras tensor.
>>> tf.keras.backend.is_keras_tensor(keras_layer_output)
True

get_uid function
tf.keras.backend.get_uid(prefix="")
Associates a string prefix with an integer counter in a TensorFlow graph.

Arguments

prefix: String prefix to index.
Returns

Unique integer ID.

Example


>>> get_uid('dense')
1
>>> get_uid('dense')
2

rnn function
tf.keras.backend.rnn(
step_function,
inputs,
initial_states,
go_backwards=False,
mask=None,
constants=None,
unroll=False,
input_length=None,
time_major=False,
zero_output_for_mask=False,
)
Iterates over the time dimension of a tensor.

Arguments

step_function: RNN step function. Args; input; Tensor with shape (samples, ...) (no time dimension), representing input for the batch of samples at a certain time step. states; List of tensors. Returns; output; Tensor with shape (samples, output_dim) (no time dimension). new_states; List of tensors, same length and shapes as 'states'. The first state in the list must be the output tensor at the previous timestep.
inputs: Tensor of temporal data of shape (samples, time, ...) (at least 3D), or nested tensors, and each of which has shape (samples, time, ...).
initial_states: Tensor with shape (samples, state_size) (no time dimension), containing the initial values for the states used in the step function. In the case that state_size is in a nested shape, the shape of initial_states will also follow the nested structure.
go_backwards: Boolean. If True, do the iteration over the time dimension in reverse order and return the reversed sequence.
mask: Binary tensor with shape (samples, time, 1), with a zero for every element that is masked.
constants: List of constant values passed at each step.
unroll: Whether to unroll the RNN or to use a symbolic while_loop.
input_length: An integer or a 1-D Tensor, depending on whether the time dimension is fixed-length or not. In case of variable length input, it is used for masking in case there's no mask specified.
time_major: Boolean. If true, the inputs and outputs will be in shape (timesteps, batch, ...), whereas in the False case, it will be (batch, timesteps, ...). Using time_major = True is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form.
zero_output_for_mask: Boolean. If True, the output for masked timestep will be zeros, whereas in the False case, output from previous timestep is returned.
Returns

A tuple, (last_output, outputs, new_states). last_output: the latest output of the rnn, of shape (samples, ...) outputs: tensor with shape (samples, time, ...) where each entry outputs[s, t] is the output of the step function at time t for sample s. new_states: list of tensors, latest states returned by the step function, of shape (samples, ...).

Raises

ValueError: if input dimension is less than 3.
ValueError: if unroll is True but input timestep is not a fixed number.
ValueError: if mask is provided (not None) but states is not provided (len(states) == 0).
Model plotting utilities
plot_model function
tf.keras.utils.plot_model(
model,
to_file="model.png",

Returns whether x is a Keras tensor.

A "Keras tensor" is a tensor that was returned by a Keras layer, (Layer class) or by Input.

Arguments

x: A candidate tensor.

Returns

A boolean: Whether the argument is a Keras tensor.

Raises

ValueError: In case x is not a symbolic tensor.

Examples

>>> np_var = np.array([1, 2])

>>> # A numpy array is not a symbolic tensor.

>>> tf.keras.backend.is_keras_tensor(np_var)

Traceback (most recent call last):

...

ValueError: Unexpectedly found an instance of type `<class 'numpy.ndarray'>`.

Expected a symbolic tensor instance.

>>> keras_var = tf.keras.backend.variable(np_var)

>>> # A variable created with the keras backend is not a Keras tensor.

>>> tf.keras.backend.is_keras_tensor(keras_var)

False

>>> keras_placeholder = tf.keras.backend.placeholder(shape=(2, 4, 5))

>>> # A placeholder is a Keras tensor.

>>> tf.keras.backend.is_keras_tensor(keras_placeholder)

True

>>> keras_input = tf.keras.layers.Input([10])

>>> # An Input is a Keras tensor.

>>> tf.keras.backend.is_keras_tensor(keras_input)

True

>>> keras_layer_output = tf.keras.layers.Dense(10)(keras_input)

>>> # Any Keras layer output is a Keras tensor.

>>> tf.keras.backend.is_keras_tensor(keras_layer_output)

True

get_uid function

tf.keras.backend.get_uid(prefix="")

Associates a string prefix with an integer counter in a TensorFlow graph.

Arguments

prefix: String prefix to index.

Returns

Unique integer ID.

Example

>>> get_uid('dense')

1

>>> get_uid('dense')

2

rnn function

tf.keras.backend.rnn(

step_function,

inputs,

initial_states,

go_backwards=False,

mask=None,

constants=None,

unroll=False,

input_length=None,

time_major=False,

zero_output_for_mask=False,

)

Iterates over the time dimension of a tensor.

Arguments

step_function: RNN step function. Args; input; Tensor with shape (samples, ...) (no time dimension), representing input for the batch of samples at a certain time step. states; List of tensors. Returns; output; Tensor with shape (samples, output_dim) (no time dimension). new_states; List of tensors, same length and shapes as 'states'. The first state in the list must be the output tensor at the previous timestep.

inputs: Tensor of temporal data of shape (samples, time, ...) (at least 3D), or nested tensors, and each of which has shape (samples, time, ...).

initial_states: Tensor with shape (samples, state_size) (no time dimension), containing the initial values for the states used in the step function. In the case that state_size is in a nested shape, the shape of initial_states will also follow the nested structure.

go_backwards: Boolean. If True, do the iteration over the time dimension in reverse order and return the reversed sequence.

mask: Binary tensor with shape (samples, time, 1), with a zero for every element that is masked.

constants: List of constant values passed at each step.

unroll: Whether to unroll the RNN or to use a symbolic while_loop.

input_length: An integer or a 1-D Tensor, depending on whether the time dimension is fixed-length or not. In case of variable length input, it is used for masking in case there's no mask specified.

time_major: Boolean. If true, the inputs and outputs will be in shape (timesteps, batch, ...), whereas in the False case, it will be (batch, timesteps, ...). Using time_major = True is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form.

zero_output_for_mask: Boolean. If True, the output for masked timestep will be zeros, whereas in the False case, output from previous timestep is returned.

Returns

A tuple, (last_output, outputs, new_states). last_output: the latest output of the rnn, of shape (samples, ...) outputs: tensor with shape (samples, time, ...) where each entry outputs[s, t] is the output of the step function at time t for sample s. new_states: list of tensors, latest states returned by the step function, of shape (samples, ...).

Raises

ValueError: if input dimension is less than 3.

ValueError: if unroll is True but input timestep is not a fixed number.

ValueError: if mask is provided (not None) but states is not provided (len(states) == 0).

Model plotting utilities

plot_model function

tf.keras.utils.plot_model(

model,

to_file="model.png",