TensorFlow cross-entropy loss

In this Python tutorial, we will learn how to calculate a Cross-Entropy loss in Python TensorFlow. Also, we will cover the following topics.

• TensorFlow cross-entropy loss with logits
• TensorFlow cross-entropy loss nan
• TensorFlow cross-entropy loss formula
• TensorFlow cross-entropy loss without softmax
• TensorFlow cross-entropy loss with mask
• TensorFlow binary cross-entropy loss
• TensorFlow Keras cross-entropy loss
• TensorFlow weighted cross-entropy loss
• sparse cross-entropy loss TensorFlow

TensorFlow cross-entropy loss

• In this section, we will discuss how to generate the cross-entropy loss between the prediction and labels.
• To perform this particular task, we are going to use the tf.Keras.losses.CategoricalCrossentropy() function and this method will help the user to get the cross-entropy loss between predicted values and label values.

Syntax:

Let’s have a look at the syntax and understand the working of tf.Keras.losses.CategoricalCrossentropy() function in Python TensorFlow.

tf.keras.losses.CategoricalCrossentropy(
    from_logits=False,
    label_smoothing=0.0,
    axis=-1,
    reduction=losses_utils.ReductionV2.AUTO,
    name='categorical_crossentropy'
)

• It consists of a few parameters.
  • from_logits: This parameter indicates the logit values and it contains probabilities values that are [0,1].
  • label_smoothing: By default, it takes 0.0 values and it will check the condition when it is greater than 0 and compute the loss between the true values.
  • axis: By default, it takes a -1 value and the axis along which to generate cross-entropy.
  • name: By default, it takes the ‘categorical_crossentropy’ value and defines the name of the operation.

Example:

Let’s take an example and check how to generate the cross-entropy loss between the prediction and labels.

Source Code:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_true = [[1.,0.],
         [1.,1.]]
new_predict = [[0.9,1.0],
         [0.3,1.0]]

new_binar_cross = tf.keras.losses.CategoricalCrossentropy()

result=new_binar_cross(new_true,new_predict)
with tf.compat.v1.Session() as val:
  new_output=val.run(result)
print(new_output)

In the above code, we have used the tf.keras.losses.CategoricalCrossentropy() function and then assign the actual and predicted values to it.

Here is the Screenshot of the following given code.

Read: TensorFlow Multiplication

TensorFlow cross-entropy loss with logits

• In this section, we are going to calculate the logits value with the help of cross-entropy in Python TensorFlow.
• To perform this particular task, we are going to use the tf.nn.softmax_cross_entropy_with_logits() function, and this method calculates the softmax cross-entropy between labels and logits.
• In this method, labels and logits have the same datatype and the axis parameter defines the class dimension.

Syntax:

Here is the Syntax of tf.nn.softmax_cross_entropy_with_logits() in Python TensorFlow.

tf.nn.softmax_cross_entropy_with_logits(
    labels, logits, axis=-1, name=None
)

• It consists of a few parameters
  • labels: This parameter indicates the class dimension and it is a valid probability distribution.
  • logits: These are typically linear output and unnormalized log probabilities.
  • axis: By default, it takes a -1 value which specifies the last dimension.
  • name: By default, it takes none value and defines the name of the operation.

Example:

Let’s take an example and check how to calculate the logits value with the help of cross-entropy in Python TensorFlow.

Source Code:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()

new_logit_val = [[7.0, 8.0, 2.0], [1.0, 6.0, 8.0]]
new_label_val = [[3.0, 2.0, 1.0], [0.0, 1.0, 0.7]]
result=tf.nn.softmax_cross_entropy_with_logits(new_label_val,new_logit_val)
with tf.compat.v1.Session() as val:
  new_output=val.run(result)
print(new_output)

In the following code, we have imported the TensorFlow library and then created the logit and label values. After that, we have used the tf.nn.softmax_cross_entropy_with_logits() function and within this function, we assigned the labels and logits.

Here is the implementation of the following given code.

Read: TensorFlow mean squared error

TensorFlow cross-entropy loss nan

• In this section, we will discuss how to detect the nan in cross-entropy loss by using Python TensorFlow.
• To perform this particular task, we are going to use the nan values in the actual tensor and then we are going to use the tf.keras.losses.CategoricalCrossentropy() function.

Example:

import tensorflow as tf
import numpy as np

tf.compat.v1.disable_eager_execution()
new_true = [[1.0,np.nan],
         [np.nan,1.]]
new_predict = [[0.9,1.0],
         [0.3,1.0]]

new_binar_cross = tf.keras.losses.CategoricalCrossentropy()

result=new_binar_cross(new_true,new_predict)
with tf.compat.v1.Session() as val:
  new_output=val.run(result)
print(new_output)

Here is the execution of the following given code.

As you can see in the Screenshot the output displays the nan value.

Read: Python TensorFlow Placeholder

TensorFlow cross-entropy loss formula

• In TensorFlow, the loss function is used to optimize the input model during training and the main purpose of this function is to minimize the loss function.
• Cross entropy loss is a cost function to optimize the model and it also takes the output probabilities and calculates the distance from the binary values.

Example:

Let’s take an example and check how to use the cross-entropy in Python TensorFlow.

Source Code:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_true = [[1.,0.],
         [1.,1.]]
new_predict = [[0.9,1.0],
         [0.3,1.0]]

new_binar_cross = tf.keras.losses.CategoricalCrossentropy()

result=new_binar_cross(new_true,new_predict)
with tf.compat.v1.Session() as val:
  new_output=val.run(result)
print(new_output)

By using the tf.Keras.losses.CategoricalCrossentropy() function and within this function we have set the new_true and new_predict values to it.

Here is the Screenshot of the following given code.

Read: Tensorflow iterate over tensor

TensorFlow cross-entropy loss without softmax

• In this section, we will discuss how to use the loss cross-entropy without softmax in Python TensorFlow.
• To perform this particular task, we are going to use the tf.Keras.losses.CategoricalCrossentropy() function and this method will help the user to get the cross-entropy loss between predicted values and label values.

Syntax:

Let’s have a look at the Syntax and understand the working of tf.Keras.losses.CategoricalCrossentropy() function in Python TensorFlow.

tf.keras.losses.CategoricalCrossentropy(
    from_logits=False,
    label_smoothing=0.0,
    axis=-1,
    reduction=losses_utils.ReductionV2.AUTO,
    name='categorical_crossentropy'
)

Example:

Let’s take an example and check how to use the loss cross-entropy without softmax in Python TensorFlow.

Source Code:

import tensorflow as tf

y_true = [1, 0, 1, 1]
y_pred = [-15.6, 2.27, 8.94, -13.8]
new_result = tf.keras.losses.BinaryCrossentropy(from_logits=True)
new_result(y_true, y_pred).numpy()

In the above code, we have imported the TensorFlow library and then use the tf.Keras.losses.BinaryCrossentropy() function and within this function we have set the logits=True.

After that, we have assigned the y_pred, y_true() values, and once you will execute this code the output displays the random value.

Here is the Screenshot of the following given code.

Read: Python TensorFlow truncated normal

TensorFlow cross-entropy loss with mask

• In this section, we will discuss how to find the cross-entropy with mask in Python TensorFlow.
• To perform this particular task, we are going to use the tf.equal() and which is used to return the tensor of boolean values for the given tensor values, and to convert the mask values we are going to use the tf.compat.v1.to_float().
• By using the tf.compat.v1.losses.softmax_cross_entropy() and this is used to create a cross entropy loss.

Syntax:

Here is the Syntax of tf.compat.v1.losses.softmax_cross_entropy() function in Python TensorFlow.

tf.compat.v1.losses.softmax_cross_entropy(
    onehot_labels,
    logits,
    weights=1.0,
    label_smoothing=0,
    scope=None,
    loss_collection=ops.GraphKeys.LOSSES,
    reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)

• It consists of a few parameters.
  • onehot_labels: This parameter indicates the one-hot encoded labels.
  • logits: This parameter specifies the logits values.
  • weights: By default, it takes a 1.0 value that is transmit to loss and it is an optional tensor.
  • label_smoothing: By default, it takes a 0 value and it will check the condition if it is more than 0 value then it will smooth the label.
  • scope: It specifies the scope of the operation and it performs the compute to loss.
  • loss_collection: This parameter indicates the collection in which the loss will be added,

Example:

Let’s take an example and check how to find the cross-entropy with mask in Python TensorFlow.

Source Code:

import tensorflow as tf

new_logit_val = [[0.0,1.0], [1.0,0.0]]
new_label_val = [[3.0, 2.0], [0.0, 1.0]]

mask = tf.equal(new_logit_val, 0)
weights = tf.compat.v1.to_float(mask) 
loss = tf.compat.v1.losses.softmax_cross_entropy(new_label_val, new_logit_val, weights)
print(loss)

In the following given code, we have used the tf.equal() function and within this function, we assigned the logit value and convert them into masks by using the tf.compat.v1.to_float().

Here is the Screenshot of the following given code.

TensorFlow binary cross-entropy loss

• In this section, we will discuss how to calculate a Binary Cross-Entropy loss in Python TensorFlow. For this, you can refer to our detailed article Binary Cross Entropy TensorFlow.
• You will get all the information regarding binary cross-entropy loss.

TensorFlow Keras cross-entropy loss

• In this section, we will discuss how to measure the cross-entropy loss in Keras.
• To perform this particular task, we are going to use the tf.Keras.losses.CategoricalCrossentropy() function and this method will help the user to get the cross-entropy loss between predicted values and label values.

Syntax:

Here is the Syntax of tf.Keras.losses.CategoricalCrossentropy() in Python TensorFlow.

tf.keras.losses.CategoricalCrossentropy(
    from_logits=False,
    label_smoothing=0.0,
    axis=-1,
    reduction=losses_utils.ReductionV2.AUTO,
    name='categorical_crossentropy'
)

Example:

Let’s take an example and check how to measure the cross-entropy loss in Keras.

Source Code:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()

new_true_val = [[1.,1.],
         [0.,1.]]
new_predict_val = [[1.0,0.0],
         [1.0,0.0]]

new_binar_cross = tf.keras.losses.CategoricalCrossentropy()

result=new_binar_cross(new_true_val,new_predict_val)
with tf.compat.v1.Session() as val:
  new_output=val.run(result)
print(new_output)

Here is the implementation of the following given code

Read: Python TensorFlow expand_dims

TensorFlow weighted cross-entropy loss

• In this section, we will discuss how to use the weights in cross-entropy loss by using Python TensorFlow.
• To perform this particular task, we are going to use the tf.nn.weighted_cross_entropy_with_logits() function and this function will help the user to find a weighted cross-entropy.

Example:

Let’s take an example and check how to use the weights in cross-entropy loss by using Python TensorFlow.

Source Code:

import tensorflow as tf

new_label = tf.constant([0.1, 1., 1.])
new_logit = tf.constant([1., -0, -9.])
result=tf.nn.weighted_cross_entropy_with_logits(
    labels=new_label, logits=new_logit, pos_weight=tf.constant(1.5))
print(result)

Here is the implementation of the following given code.

Read: Python TensorFlow reduce_mean

Sparse cross-entropy loss TensorFlow

• In this Program, we will discuss how to sparse a cross-entropy loss in Python TensorFlow.
• To perform this particular task, we are going to use the tf.keras.losses.SparseCategoricalCrossentropy() function and this method is used to find the cross-entropy loss between the prediction and labels.

Syntax:

Let’s have a look at the Syntax and understand the working of tf.keras.losses.SparseCategoricalCrossentropy() function in Python TensorFlow.

tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=False,
    reduction=losses_utils.ReductionV2.AUTO,
    name='sparse_categorical_crossentropy'
)

• It consists of a few parameters.
  • from_logits: This parameter indicates the y_prediction encodes a probability distribution and by default, it takes a false value.
  • name: By default, it takes ‘sparse_categorical_crossentropy’ and specifies the name of the operation.

Example:

Let’s take an example and check how to sparse a cross-entropy loss in Python TensorFlow.

Source Code:

import tensorflow as tf 

new_true = [2, 1]
new_prediction = [[0.1, 1.2, 1.2], [1.0, 2.1, 0.95]]
new_result = tf.keras.losses.SparseCategoricalCrossentropy()
new_result(new_true, new_prediction)

Here is the implementation of the following given code

Also, take a look at some more TensorFlow tutorials.

• Python TensorFlow reduce_sum
• TensorFlow Tensor to numpy
• Python TensorFlow one_hot
• TensorFlow Fully Connected Layer

In this Python tutorial, we have learned how to calculate a Cross-Entropy loss in Python TensorFlow. Also, we have covered the following topics.

• TensorFlow cross-entropy loss with logits
• TensorFlow cross-entropy loss nan
• TensorFlow cross-entropy loss formula
• TensorFlow cross-entropy loss without softmax
• TensorFlow cross-entropy loss with mask
• TensorFlow binary cross-entropy loss
• TensorFlow Keras cross-entropy loss
• TensorFlow weighted cross-entropy loss
• sparse cross-entropy loss TensorFlow