Character Level Cnn Model - Data Profiler v0.4.4

class dataprofiler.labelers.character_level_cnn_model.NoV1ResourceMessageFilter(name='')¶

Bases: logging.Filter

Removes TF2 warning for using TF1 model which has resources.

Initialize a filter.

Initialize with the name of the logger which, together with its children, will have its events allowed through the filter. If no name is specified, allow every event.

filter(record)¶

Determine if the specified record is to be logged.

Returns True if the record should be logged, or False otherwise. If deemed appropriate, the record may be modified in-place.

class dataprofiler.labelers.character_level_cnn_model.FBetaScore(*args, **kwargs)¶

Bases: tensorflow.python.keras.metrics.Metric

Computes F-Beta score. Adapted and slightly modified from https://github.com/tensorflow/addons/blob/v0.12.0/tensorflow_addons/metrics/f_scores.py#L211-L283

# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the “License”); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # https://github.com/tensorflow/addons/blob/v0.12.0/LICENSE # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an “AS IS” BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ==============================================================================

It is the weighted harmonic mean of precision and recall. Output range is [0, 1]. Works for both multi-class and multi-label classification. $$ F_{beta} = (1 + beta^2) * frac{textrm{precision} * textrm{precision}}{(beta^2 cdot textrm{precision}) + textrm{recall}} $$ :param num_classes: Number of unique classes in the dataset. :param average: Type of averaging to be performed on data.

Acceptable values are None, micro, macro and weighted. Default value is None.

Parameters

beta – Determines the weight of precision and recall in harmonic mean. Determines the weight given to the precision and recall. Default value is 1.
threshold – Elements of y_pred greater than threshold are converted to be 1, and the rest 0. If threshold is None, the argmax is converted to 1, and the rest 0.
name – (Optional) String name of the metric instance.
dtype – (Optional) Data type of the metric result.

Returns

float.

Return type

F-Beta Score

update_state(y_true, y_pred, sample_weight=None)¶

Accumulates statistics for the metric.

Note: This function is executed as a graph function in graph mode. This means:

Operations on the same resource are executed in textual order. This should make it easier to do things like add the updated value of a variable to another, for example.

You don’t need to worry about collecting the update ops to execute. All update ops added to the graph by this function will be executed.

As a result, code should generally work the same way with graph or eager execution.

Parameters

*args –
**kwargs – A mini-batch of inputs to the Metric.

result()¶

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

get_config()¶: Returns the serializable config of the metric.

reset_states()¶

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

property activity_regularizer¶: Optional regularizer function for the output of this layer.

add_loss(losses, **kwargs)¶

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

```python class MyLayer(tf.keras.layers.Layer):

def call(self, inputs):
self.add_loss(tf.abs(tf.reduce_mean(inputs))) return inputs

```

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Input`s. These losses become part of the model’s topology and are tracked in `get_config.

Example:

`python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) `

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

`python inputs = tf.keras.Input(shape=(10,)) d = tf.keras.layers.Dense(10) x = d(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(d.kernel)) `

Parameters

losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor.
**kwargs –
Additional keyword arguments for backward compatibility. Accepted values:

inputs - Deprecated, will be automatically inferred.

add_metric(value, name=None, **kwargs)¶

Adds metric tensor to the layer.

This method can be used inside the call() method of a subclassed layer or model.

```python class MyMetricLayer(tf.keras.layers.Layer):

def __init__(self):
super(MyMetricLayer, self).__init__(name=’my_metric_layer’) self.mean = tf.keras.metrics.Mean(name=’metric_1’)

def call(self, inputs):
self.add_metric(self.mean(x)) self.add_metric(tf.reduce_sum(x), name=’metric_2’) return inputs

```

This method can also be called directly on a Functional Model during construction. In this case, any tensor passed to this Model must be symbolic and be able to be traced back to the model’s Input`s. These metrics become part of the model’s topology and are tracked when you save the model via `save().

`python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) model.add_metric(math_ops.reduce_sum(x), name='metric_1') `

Note: Calling add_metric() with the result of a metric object on a Functional Model, as shown in the example below, is not supported. This is because we cannot trace the metric result tensor back to the model’s inputs.

`python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) model.add_metric(tf.keras.metrics.Mean()(x), name='metric_1') `

Parameters

value – Metric tensor.
name – String metric name.
**kwargs – Additional keyword arguments for backward compatibility. Accepted values: aggregation - When the value tensor provided is not the result of calling a keras.Metric instance, it will be aggregated by default using a keras.Metric.Mean.

add_update(updates, inputs=None)¶

Add update op(s), potentially dependent on layer inputs.

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters

updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode.
inputs – Deprecated, will be automatically inferred.

add_variable(*args, **kwargs)¶: Deprecated, do NOT use! Alias for add_weight.

add_weight(name, shape=(), aggregation=<VariableAggregation.SUM: 1>, synchronization=<VariableSynchronization.ON_READ: 3>, initializer=None, dtype=None)¶: Adds state variable. Only for use by subclasses.

apply(inputs, *args, **kwargs)¶

Deprecated, do NOT use!

This is an alias of self.__call__.

Parameters

inputs – Input tensor(s).
*args – additional positional arguments to be passed to self.call.
**kwargs – additional keyword arguments to be passed to self.call.

Returns

Output tensor(s).

build(input_shape)¶

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

call(inputs, **kwargs)¶

This is where the layer’s logic lives.

Note here that call() method in tf.keras is little bit different from keras API. In keras API, you can pass support masking for layers as additional arguments. Whereas tf.keras has compute_mask() method to support masking.

Parameters

inputs – Input tensor, or list/tuple of input tensors.
**kwargs – Additional keyword arguments. Currently unused.

Returns

A tensor or list/tuple of tensors.

property compute_dtype¶

The dtype of the layer’s computations.

This is equivalent to Layer.dtype_policy.compute_dtype. Unless mixed precision is used, this is the same as Layer.dtype, the dtype of the weights.

Layers automatically cast their inputs to the compute dtype, which causes computations and the output to be in the compute dtype as well. This is done by the base Layer class in Layer.__call__, so you do not have to insert these casts if implementing your own layer.

Layers often perform certain internal computations in higher precision when compute_dtype is float16 or bfloat16 for numeric stability. The output will still typically be float16 or bfloat16 in such cases.

Returns: The layer’s compute dtype.

compute_mask(inputs, mask=None)¶

Computes an output mask tensor.

Parameters

inputs – Tensor or list of tensors.
mask – Tensor or list of tensors.

Returns

None or a tensor (or list of tensors,: one per output tensor of the layer).

compute_output_shape(input_shape)¶

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.
Returns: An input shape tuple.

compute_output_signature(input_signature)¶

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters

input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer.

Returns

Single TensorSpec or nested structure of TensorSpec objects, describing: how the layer would transform the provided input.

Raises

TypeError – If input_signature contains a non-TensorSpec object.

count_params()¶

Count the total number of scalars composing the weights.

Returns: An integer count.
Raises: ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).

property dtype¶

The dtype of the layer weights.

This is equivalent to Layer.dtype_policy.variable_dtype. Unless mixed precision is used, this is the same as Layer.compute_dtype, the dtype of the layer’s computations.

property dtype_policy¶

The dtype policy associated with this layer.

This is an instance of a tf.keras.mixed_precision.Policy.

property dynamic¶: Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)¶

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config.
Returns: A layer instance.

get_input_at(node_index)¶

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A tensor (or list of tensors if the layer has multiple inputs).
Raises: RuntimeError – If called in Eager mode.

get_input_mask_at(node_index)¶

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A mask tensor (or list of tensors if the layer has multiple inputs).

get_input_shape_at(node_index)¶

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A shape tuple (or list of shape tuples if the layer has multiple inputs).
Raises: RuntimeError – If called in Eager mode.

get_losses_for(inputs)¶

Deprecated, do NOT use!

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors.
Returns: List of loss tensors of the layer that depend on inputs.

get_output_at(node_index)¶

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A tensor (or list of tensors if the layer has multiple outputs).
Raises: RuntimeError – If called in Eager mode.

get_output_mask_at(node_index)¶

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A mask tensor (or list of tensors if the layer has multiple outputs).

get_output_shape_at(node_index)¶

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A shape tuple (or list of shape tuples if the layer has multiple outputs).
Raises: RuntimeError – If called in Eager mode.

get_updates_for(inputs)¶

Deprecated, do NOT use!

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors.
Returns: List of update ops of the layer that depend on inputs.

get_weights()¶

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
       [2.],
       [2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.

property inbound_nodes¶: Deprecated, do NOT use! Only for compatibility with external Keras.

property input¶

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns

Input tensor or list of input tensors.

Raises

RuntimeError – If called in Eager mode.
AttributeError – If no inbound nodes are found.

property input_mask¶

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns

Input mask tensor (potentially None) or list of input mask tensors.

Raises

AttributeError – if the layer is connected to
more than one incoming layers. –

property input_shape¶

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

Raises

AttributeError – if the layer has no defined input_shape.
RuntimeError – if called in Eager mode.

property input_spec¶

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

`python self.input_spec = tf.keras.layers.InputSpec(ndim=4) `

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

` ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] `

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

For more information, see tf.keras.layers.InputSpec.

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.

property losses¶

List of losses added using the add_loss() API.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Examples:

>>> class MyLayer(tf.keras.layers.Layer):
...   def call(self, inputs):
...     self.add_loss(tf.abs(tf.reduce_mean(inputs)))
...     return inputs
>>> l = MyLayer()
>>> l(np.ones((10, 1)))
>>> l.losses
[1.0]

>>> inputs = tf.keras.Input(shape=(10,))
>>> x = tf.keras.layers.Dense(10)(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Activity regularization.
>>> len(model.losses)
0
>>> model.add_loss(tf.abs(tf.reduce_mean(x)))
>>> len(model.losses)
1

>>> inputs = tf.keras.Input(shape=(10,))
>>> d = tf.keras.layers.Dense(10, kernel_initializer='ones')
>>> x = d(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Weight regularization.
>>> model.add_loss(lambda: tf.reduce_mean(d.kernel))
>>> model.losses
[<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]

Returns: A list of tensors.

property metrics¶

List of metrics added using the add_metric() API.

Example:

>>> input = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2)
>>> output = d(input)
>>> d.add_metric(tf.reduce_max(output), name='max')
>>> d.add_metric(tf.reduce_min(output), name='min')
>>> [m.name for m in d.metrics]
['max', 'min']

Returns: A list of Metric objects.

property name¶: Name of the layer (string), set in the constructor.

property name_scope¶: Returns a tf.name_scope instance for this class.

property non_trainable_variables¶

property non_trainable_weights¶

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns: A list of non-trainable variables.

property outbound_nodes¶: Deprecated, do NOT use! Only for compatibility with external Keras.

property output¶

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns

Output tensor or list of output tensors.

Raises

AttributeError – if the layer is connected to more than one incoming layers.
RuntimeError – if called in Eager mode.

property output_mask¶

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns

Output mask tensor (potentially None) or list of output mask tensors.

Raises

AttributeError – if the layer is connected to
more than one incoming layers. –

property output_shape¶

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

Raises

AttributeError – if the layer has no defined output shape.
RuntimeError – if called in Eager mode.

set_weights(weights)¶

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
       [2.],
       [2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights).
Raises: ValueError – If the provided weights list does not match the layer’s specifications.

property stateful¶

property submodules¶

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.

property supports_masking¶: Whether this layer supports computing a mask using compute_mask.

property trainable¶

property trainable_variables¶

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).

property trainable_weights¶

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns: A list of trainable variables.

property updates¶

property variable_dtype¶: Alias of Layer.dtype, the dtype of the weights.

property variables¶

Returns the list of all layer variables/weights.

Alias of self.weights.

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns: A list of variables.

property weights¶

Returns the list of all layer variables/weights.

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns: A list of variables.

classmethod with_name_scope(method)¶

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)

Using the above module would produce `tf.Variable`s and `tf.Tensor`s whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap.
Returns: The original method wrapped such that it enters the module’s name scope.

class dataprofiler.labelers.character_level_cnn_model.F1Score(*args, **kwargs)¶

Bases: dataprofiler.labelers.character_level_cnn_model.FBetaScore

Computes F-1 Score.

# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the “License”); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # https://github.com/tensorflow/addons/blob/v0.12.0/LICENSE # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an “AS IS” BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ==============================================================================

It is the harmonic mean of precision and recall. Output range is [0, 1]. Works for both multi-class and multi-label classification. $$ F_1 = 2 cdot frac{textrm{precision} cdot textrm{recall}}{textrm{precision} + textrm{recall}} $$ :param num_classes: Number of unique classes in the dataset. :param average: Type of averaging to be performed on data.

Acceptable values are None, micro, macro and weighted. Default value is None.

Parameters

threshold – Elements of y_pred above threshold are considered to be 1, and the rest 0. If threshold is None, the argmax is converted to 1, and the rest 0.
name – (Optional) String name of the metric instance.
dtype – (Optional) Data type of the metric result.

Returns

float.

Return type

F-1 Score

get_config()¶: Returns the serializable config of the metric.

property activity_regularizer¶: Optional regularizer function for the output of this layer.

add_loss(losses, **kwargs)¶

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

```python class MyLayer(tf.keras.layers.Layer):

def call(self, inputs):
self.add_loss(tf.abs(tf.reduce_mean(inputs))) return inputs

```

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Input`s. These losses become part of the model’s topology and are tracked in `get_config.

Example:

`python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) `

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

`python inputs = tf.keras.Input(shape=(10,)) d = tf.keras.layers.Dense(10) x = d(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(d.kernel)) `

Parameters

losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor.
**kwargs –
Additional keyword arguments for backward compatibility. Accepted values:

inputs - Deprecated, will be automatically inferred.

add_metric(value, name=None, **kwargs)¶

Adds metric tensor to the layer.

This method can be used inside the call() method of a subclassed layer or model.

```python class MyMetricLayer(tf.keras.layers.Layer):

def __init__(self):
super(MyMetricLayer, self).__init__(name=’my_metric_layer’) self.mean = tf.keras.metrics.Mean(name=’metric_1’)

def call(self, inputs):
self.add_metric(self.mean(x)) self.add_metric(tf.reduce_sum(x), name=’metric_2’) return inputs

```

This method can also be called directly on a Functional Model during construction. In this case, any tensor passed to this Model must be symbolic and be able to be traced back to the model’s Input`s. These metrics become part of the model’s topology and are tracked when you save the model via `save().

`python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) model.add_metric(math_ops.reduce_sum(x), name='metric_1') `

Note: Calling add_metric() with the result of a metric object on a Functional Model, as shown in the example below, is not supported. This is because we cannot trace the metric result tensor back to the model’s inputs.

`python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) model.add_metric(tf.keras.metrics.Mean()(x), name='metric_1') `

Parameters

value – Metric tensor.
name – String metric name.
**kwargs – Additional keyword arguments for backward compatibility. Accepted values: aggregation - When the value tensor provided is not the result of calling a keras.Metric instance, it will be aggregated by default using a keras.Metric.Mean.

add_update(updates, inputs=None)¶

Add update op(s), potentially dependent on layer inputs.

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters

updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode.
inputs – Deprecated, will be automatically inferred.

add_variable(*args, **kwargs)¶: Deprecated, do NOT use! Alias for add_weight.

add_weight(name, shape=(), aggregation=<VariableAggregation.SUM: 1>, synchronization=<VariableSynchronization.ON_READ: 3>, initializer=None, dtype=None)¶: Adds state variable. Only for use by subclasses.

apply(inputs, *args, **kwargs)¶

Deprecated, do NOT use!

This is an alias of self.__call__.

Parameters

inputs – Input tensor(s).
*args – additional positional arguments to be passed to self.call.
**kwargs – additional keyword arguments to be passed to self.call.

Returns

Output tensor(s).

build(input_shape)¶

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters: input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

call(inputs, **kwargs)¶

This is where the layer’s logic lives.

Note here that call() method in tf.keras is little bit different from keras API. In keras API, you can pass support masking for layers as additional arguments. Whereas tf.keras has compute_mask() method to support masking.

Parameters

inputs – Input tensor, or list/tuple of input tensors.
**kwargs – Additional keyword arguments. Currently unused.

Returns

A tensor or list/tuple of tensors.

property compute_dtype¶

The dtype of the layer’s computations.

This is equivalent to Layer.dtype_policy.compute_dtype. Unless mixed precision is used, this is the same as Layer.dtype, the dtype of the weights.

Layers automatically cast their inputs to the compute dtype, which causes computations and the output to be in the compute dtype as well. This is done by the base Layer class in Layer.__call__, so you do not have to insert these casts if implementing your own layer.

Layers often perform certain internal computations in higher precision when compute_dtype is float16 or bfloat16 for numeric stability. The output will still typically be float16 or bfloat16 in such cases.

Returns: The layer’s compute dtype.

compute_mask(inputs, mask=None)¶

Computes an output mask tensor.

Parameters

inputs – Tensor or list of tensors.
mask – Tensor or list of tensors.

Returns

None or a tensor (or list of tensors,: one per output tensor of the layer).

compute_output_shape(input_shape)¶

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters: input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.
Returns: An input shape tuple.

compute_output_signature(input_signature)¶

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters

input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer.

Returns

Single TensorSpec or nested structure of TensorSpec objects, describing: how the layer would transform the provided input.

Raises

TypeError – If input_signature contains a non-TensorSpec object.

count_params()¶

Count the total number of scalars composing the weights.

Returns: An integer count.
Raises: ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).

property dtype¶

The dtype of the layer weights.

This is equivalent to Layer.dtype_policy.variable_dtype. Unless mixed precision is used, this is the same as Layer.compute_dtype, the dtype of the layer’s computations.

property dtype_policy¶

The dtype policy associated with this layer.

This is an instance of a tf.keras.mixed_precision.Policy.

property dynamic¶: Whether the layer is dynamic (eager-only); set in the constructor.

classmethod from_config(config)¶

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters: config – A Python dictionary, typically the output of get_config.
Returns: A layer instance.

get_input_at(node_index)¶

Retrieves the input tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A tensor (or list of tensors if the layer has multiple inputs).
Raises: RuntimeError – If called in Eager mode.

get_input_mask_at(node_index)¶

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A mask tensor (or list of tensors if the layer has multiple inputs).

get_input_shape_at(node_index)¶

Retrieves the input shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A shape tuple (or list of shape tuples if the layer has multiple inputs).
Raises: RuntimeError – If called in Eager mode.

get_losses_for(inputs)¶

Deprecated, do NOT use!

Retrieves losses relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors.
Returns: List of loss tensors of the layer that depend on inputs.

get_output_at(node_index)¶

Retrieves the output tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A tensor (or list of tensors if the layer has multiple outputs).
Raises: RuntimeError – If called in Eager mode.

get_output_mask_at(node_index)¶

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A mask tensor (or list of tensors if the layer has multiple outputs).

get_output_shape_at(node_index)¶

Retrieves the output shape(s) of a layer at a given node.

Parameters: node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.
Returns: A shape tuple (or list of shape tuples if the layer has multiple outputs).
Raises: RuntimeError – If called in Eager mode.

get_updates_for(inputs)¶

Deprecated, do NOT use!

Retrieves updates relevant to a specific set of inputs.

Parameters: inputs – Input tensor or list/tuple of input tensors.
Returns: List of update ops of the layer that depend on inputs.

get_weights()¶

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
       [2.],
       [2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]

Returns: Weights values as a list of numpy arrays.

property inbound_nodes¶: Deprecated, do NOT use! Only for compatibility with external Keras.

property input¶

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns

Input tensor or list of input tensors.

Raises

RuntimeError – If called in Eager mode.
AttributeError – If no inbound nodes are found.

property input_mask¶

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns

Input mask tensor (potentially None) or list of input mask tensors.

Raises

AttributeError – if the layer is connected to
more than one incoming layers. –

property input_shape¶

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

Raises

AttributeError – if the layer has no defined input_shape.
RuntimeError – if called in Eager mode.

property input_spec¶

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

`python self.input_spec = tf.keras.layers.InputSpec(ndim=4) `

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

` ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] `

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

For more information, see tf.keras.layers.InputSpec.

Returns: A tf.keras.layers.InputSpec instance, or nested structure thereof.

property losses¶

List of losses added using the add_loss() API.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Examples:

>>> class MyLayer(tf.keras.layers.Layer):
...   def call(self, inputs):
...     self.add_loss(tf.abs(tf.reduce_mean(inputs)))
...     return inputs
>>> l = MyLayer()
>>> l(np.ones((10, 1)))
>>> l.losses
[1.0]

>>> inputs = tf.keras.Input(shape=(10,))
>>> x = tf.keras.layers.Dense(10)(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Activity regularization.
>>> len(model.losses)
0
>>> model.add_loss(tf.abs(tf.reduce_mean(x)))
>>> len(model.losses)
1

>>> inputs = tf.keras.Input(shape=(10,))
>>> d = tf.keras.layers.Dense(10, kernel_initializer='ones')
>>> x = d(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Weight regularization.
>>> model.add_loss(lambda: tf.reduce_mean(d.kernel))
>>> model.losses
[<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]

Returns: A list of tensors.

property metrics¶

List of metrics added using the add_metric() API.

Example:

>>> input = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2)
>>> output = d(input)
>>> d.add_metric(tf.reduce_max(output), name='max')
>>> d.add_metric(tf.reduce_min(output), name='min')
>>> [m.name for m in d.metrics]
['max', 'min']

Returns: A list of Metric objects.

property name¶: Name of the layer (string), set in the constructor.

property name_scope¶: Returns a tf.name_scope instance for this class.

property non_trainable_variables¶

property non_trainable_weights¶

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns: A list of non-trainable variables.

property outbound_nodes¶: Deprecated, do NOT use! Only for compatibility with external Keras.

property output¶

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns

Output tensor or list of output tensors.

Raises

AttributeError – if the layer is connected to more than one incoming layers.
RuntimeError – if called in Eager mode.

property output_mask¶

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns

Output mask tensor (potentially None) or list of output mask tensors.

Raises

AttributeError – if the layer is connected to
more than one incoming layers. –

property output_shape¶

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

Raises

AttributeError – if the layer has no defined output shape.
RuntimeError – if called in Eager mode.

reset_states()¶

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result()¶

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

set_weights(weights)¶

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
       [2.],
       [2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]

Parameters: weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights).
Raises: ValueError – If the provided weights list does not match the layer’s specifications.

property stateful¶

property submodules¶

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns: A sequence of all submodules.

property supports_masking¶: Whether this layer supports computing a mask using compute_mask.

property trainable¶

property trainable_variables¶

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).

property trainable_weights¶

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns: A list of trainable variables.

update_state(y_true, y_pred, sample_weight=None)¶

Accumulates statistics for the metric.

Note: This function is executed as a graph function in graph mode. This means:

Operations on the same resource are executed in textual order. This should make it easier to do things like add the updated value of a variable to another, for example.

You don’t need to worry about collecting the update ops to execute. All update ops added to the graph by this function will be executed.

As a result, code should generally work the same way with graph or eager execution.

Parameters

*args –
**kwargs – A mini-batch of inputs to the Metric.

property updates¶

property variable_dtype¶: Alias of Layer.dtype, the dtype of the weights.

property variables¶

Returns the list of all layer variables/weights.

Alias of self.weights.

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns: A list of variables.

property weights¶

Returns the list of all layer variables/weights.

Note: This will not track the weights of nested tf.Modules that are not themselves Keras layers.

Returns: A list of variables.

classmethod with_name_scope(method)¶

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)

Using the above module would produce `tf.Variable`s and `tf.Tensor`s whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Parameters: method – The method to wrap.
Returns: The original method wrapped such that it enters the module’s name scope.

dataprofiler.labelers.character_level_cnn_model.build_embd_dictionary(filename)¶

Returns a numpy embedding dictionary from embed file with GloVe-like format

Parameters: filename (str) – Path to the embed file for loading

dataprofiler.labelers.character_level_cnn_model.create_glove_char(n_dims, source_file=None)¶

Embeds GloVe chars embeddings from source file to n_dims principal components in a new file

Parameters

n_dims (int) – Final number of principal component dims of the embeddings
source_file (str) – Location of original embeddings to factor down

class dataprofiler.labelers.character_level_cnn_model.CharacterLevelCnnModel(label_mapping=None, parameters=None)¶

Bases: dataprofiler.labelers.base_model.BaseTrainableModel

CNN Model Initializer. initialize epoch_id

Parameters

label_mapping (dict) – maps labels to their encoded integers
parameters (dict) –
Contains all the appropriate parameters for the model. Must contain num_labels. Other possible parameters are:

max_length, max_char_encoding_id, dim_embed, size_fc dropout, size_conv, num_fil, optimizer, default_label

Returns

None

requires_zero_mapping = True¶

set_label_mapping(label_mapping)¶

Sets the labels for the model

Parameters: label_mapping (dict) – label mapping of the model
Returns: None

save_to_disk(dirpath)¶

Saves whole model to disk with weights

Parameters: dirpath (str) – directory path where you want to save the model to
Returns: None

classmethod load_from_disk(dirpath)¶

Loads whole model from disk with weights

Parameters: dirpath (str) – directory path where you want to load the model from
Returns: None

reset_weights()¶

Reset the weights of the model.

Returns: None

fit(train_data, val_data=None, batch_size=32, label_mapping=None, reset_weights=False, verbose=True)¶

Train the current model with the training data and validation data

Parameters

train_data (Union[list, np.ndarray]) – Training data used to train model
val_data (Union[list, np.ndarray]) – Validation data used to validate the training
batch_size (int) – Used to determine number of samples in each batch
label_mapping (Union[dict, None]) – maps labels to their encoded integers
reset_weights (bool) – Flag to determine whether to reset the weights or not
verbose (bool) – Flag to determine whether to print status or not

Returns

None

add_label(label, same_as=None)¶

Adds a label to the data labeler.

Parameters

label (str) – new label being added to the data labeler
same_as (str) – label to have the same encoding index as for multi-label to single encoding index.

Returns

None

classmethod get_class(class_name)¶

get_parameters(param_list=None)¶: Returns a dict of parameters from the model given a list. :param param_list: list of parameters to retrieve from the model. :type param_list: list :return: dict of parameters

classmethod help()¶

Help function describing alterable parameters.

Returns: None

property label_mapping¶

mapping of labels to their encoded values

Type: return

property labels¶: Retrieves the label :return: list of labels

property num_labels¶

predict(data, batch_size=32, show_confidences=False, verbose=True)¶

Run model and get predictions

Parameters

data (Union[list, numpy.ndarray]) – text input
batch_size (int) – number of samples in the batch of data
show_confidences – whether user wants prediction confidences
verbose (bool) – Flag to determine whether to print status or not

Returns

char level predictions and confidences

Return type

dict

property reverse_label_mapping¶

Reversed order of current labels, useful for when needed to extract Labels via indices

Type: return

set_params(**kwargs)¶: Given kwargs, set the parameters if they exist.

details()¶: Prints the relevant details of the model (summary, parameters, label mapping)

Character Level Cnn Model¶