Module ‘tensorflow’ has no attribute ‘sparse_placeholder’

In this Python tutorial, we will focus on how to fix the attributeerror: Module ‘tensorflow’ has no attribute ‘sparse_placeholder’ in our model, and also we will look at some examples of how we can use the tf.sparse_placeholder() function in TensorFlow. And we will cover these topics.

• Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_placeholder’
• Attributeerror: module ‘tensorflow’ has no attribute ‘sparsetensorvalue’
• Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_to_dense’
• Attributeerror: module ‘tensorflow’ has no attribute ‘sparse’
• Attributeerror: module ‘tensorflow.keras.losses’ has no attribute ‘sparsecategoricalcrossentropy’

Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_placeholder’

• A placeholder is simply a variable to which we will subsequently give information.
• Without requiring the data, it enables us to design our computation graph and produce our operations and then we use these placeholders to feed data into the graph in the TensorFlow language.
• The sparse_placeholder inserts a placeholder for a sparse tensor that will always be fed in the order of the dictionary and it will return the sparse tensor that may be used as a handle for feeding a value.

Example:

Let’s take an example and check how we can solve the attribute error module ‘tensorflow’ has no attribute ‘sparse_placeholder’

import tensorflow as tf

result= tf.sparse_placeholder(shape=(3,3))
print(result)

Here is the Screenshot of the following given code

The solution to this error

In this example, we are going to use the tf.compat.v1.sparse_placeholder() function and this function is available in TensorFlow 2.x version.

Syntax:

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

tf.compat.v1.sparse_placeholder(
    dtype, shape=None, name=None
)

• It consists of a few parameters
  • dtype: This parameter defines the type of values elements in the input tensor to be fed in the order of dictionary.
  • shape: It defines the shape of the input tensor and it will check the condition if the given shape is not specified then you can easily feed a sparse tensor of any shape.
  • name: It specifies the name of the operation and by default, it takes none value.

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

result= tf.compat.v1.sparse_placeholder(dtype=tf.float32, shape=(3,3))
print(result)

In this example, we have imported the tensorflow library with the alias name ‘tf’ and then used the tf.compat.v1.sparse_placeholder() function If this sparse tensor is evaluated, an error will result. Feeding its value into Session.run requires the optional feed dict argument.

You can refer to the below Screenshot

This is how we can solve the attributeerror: module ‘tensorflow’ has no attribute ‘sparse_placeholder’

Read: Module ‘tensorflow’ has no attribute ‘get_variable’

Attributeerror: module ‘tensorflow’ has no attribute ‘sparsetensorvalue’

• Let us discuss how to solve the attribuiterror module ‘tensorflow’ has no attribute ‘sparsetensorvalue’.
• Indexes, values, and shapes are three distinct dense tensors that TensorFlow uses to represent a sparse tensor. For simplicity of use, the three tensors are combined into a SparseTensor class in Python.
• Before giving them to the operations below, encapsulate any independent indices, values, and shape tensors in a SparseTensor object.

Syntax:

Let’s have a look at the syntax and understand the working of the ‘tf.compat.v1.sparsetensorvalue’ function in Python TensorFlow

tf.compat.v1.SparseTensorValue(
    indices, values, dense_shape
)

• It consists of a few parameters
  • indices: a 2-D int64 tensor with the shape [N, ndims] that lists the indices of the sparse tensor’s members with nonzero values (elements are zero-indexed). Indices=[[7,8], [5,2]] designates, for instance, that the items with the indexes [[7,8], [5,2]] have nonzero values.
  • values: Any type and shape of 1-D tensor [N], which provides the values for each element in indices. For instance, the parameter values=[15, 45] indicates that element [2,6] of the sparse tensor has a value of 15, and element [13,11] of the tensor has a value of 45, given indices=[[1,3], [2,6]].
  • dense_shape: It specifies the name tuple alias for the field number.

Example:

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

result= tf.SparseTensorValue(indices=[0,1,2],values=[23,45,67],dense_shape=(3,3))
print(result)

In the following given code first, we have imported the tensorflow library and then used the tf.SparseTensorValue() function and within this function, we assigned the indices, values, and dense_shape as an argument.

Here is the Screenshot of the following given code.

Here is the Solution to this error.

In this example we are going to use the tf.compat.v1.SparseTensorValue() function and this function only works in TensorFlow latest version 2.x.

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

result= tf.compat.v1.SparseTensorValue(indices=[0,1,2],values=[23,45,67],dense_shape=(3,3))
print(result)

Here is the implementation of the following given code.

As you can see in the Screenshot the error has been solved attributeerror module tensorflow has no attribute sparsetensorvalue.

Read: Module ‘tensorflow’ has no attribute ‘log’

Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_to_dense’

• Here we will discuss how to solve the attributeerror module ‘tensorflow’ has no attribute ‘sparse_to_dense’.

Example:

import tensorflow as tf

input_tensor = tf.sparse_to_dense(dense_shape=[2, 2],values=[67, 18, 14],indices =[[1, 0],[0, 3],[2, 0]])
print(input_tensor)

Here is the Output of the following given code

The solution to this error

In this example, we will use the tf.compat.v1.sparse_to_dense() function and it will convert a sparse into a dense tensor.

Syntax:

tf.compat.v1.sparse_to_dense(
    sparse_indices,
    output_shape,
    sparse_values,
    default_value=0,
    validate_indices=True,
    name=None
)

• It consists of a few parameters
  • sparse_indices: a tensor of type int32 or int64 that is one and two-dimensional. The whole index where sparse values[i] will be stored is contained in the sparse indices[i] array.
  • output_shape: It defines the shape of the dense output tensor.
  • sparse_values: It is used for all sparse indices.
  • default_value: It defines the same type as sparse values, a 0-D Tensor. For indices not listed in sparse indices, a value should be set. zero is the default.
  • validate_indices: By default, it takes the true value and it will check the condition if it is true then the indices are checked to make sure they are sorted.

import tensorflow as tf

input_tensor = tf.sparse.SparseTensor(indices=[[1, 0], [1, 1], [1, 1],
                                                [0, 1], [3, 0], [2, 1],
                                                [1, 0]],
                                       values=[45, 20, 34, 24, 19, 25, 75],
                                       dense_shape=[5, 4])

new_input = tf.constant([90, 80, 60, 17, 2])
inputs = [new_input, input_tensor]

result = tf.compat.v1.sparse_to_dense(sparse_indices=inputs[1].indices,
                                                       output_shape=inputs[1].dense_shape,
                                                       sparse_values=inputs[1].values)

print("sparse_dense:",result)

In the following given code first, we have created a sparse tensor by using the tf.sparse.SparseTensor() and within this function we assigned the values and dense_shape() as an argument. Next, we used the tf.compat.v1.sparse_to_dense() function and get the dense tensor.

Here is the execution of the following given code

This is how we can solve the attributeerror module tensorflow that has no attribute sparse_to_dense.

Read: Attributeerror: module ‘tensorflow’ has no attribute ‘mul’

Attributeerror: module ‘tensorflow’ has no attribute ‘Sparse’

• A dataset called a sparse tensor is one in which the majority of the entries are zero; an illustration of this would be a big diagonal matrix. (the majority of which are zero).
• The non-zero values and their related coordinates are stored rather than the entire set of values for the tensor object.

Example:

import tensorflow as tf 

result=tf.Sparse(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
print(result)

You can refer to the below Screenshot

The solution to this error.

import tensorflow as tf 

result=tf.sparse.SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
print(result)

In the following given code first, we imported the tensorflow library and then used the tf.sparse.SparseTensor() and defines the indices and values to it.

Here is the implementation of the following given code

Read: Module ‘tensorflow’ has no attribute ‘div’

Attributeerror: module ‘tensorflow.keras.losses’ has no attribute ‘sparsecategoricalcrossentropy’

• In this section, we will discuss the attributeerror module ‘tensorflow.keras.losses’ has no attribute ‘sparsecategoricalcrossentropy’.
• In situations where there are two or more label classes, use this cross-entropy loss function. Labels must be supplied as integers, as is expected. Use categorical cross-entropy loss if you want to supply labels with one-hot representation.
• For y pred, there should be n floating point values per feature and for y true, there should only be one floating point value per feature.

Example:

import tensorflow as tf 

 
from tensorflow.keras import datasets, layers, models 

import matplotlib.pyplot as plt 

(new_train_images, new_train_labels), (new_test_images, new_test_labels) = datasets.cifar10.load_data() 

 
 

new_train_images, new_test_images = new_train_images / 255.0, new_test_images / 255.0 

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 

               'dog', 'frog', 'horse', 'ship', 'truck'] 

 
plt.figure(figsize=(10,10)) 

for i in range(25): 

    plt.subplot(5,5,i+1) 

    plt.xticks([]) 

    plt.yticks([]) 

    plt.grid(False) 

    plt.imshow(new_train_images[i]) 

    plt.xlabel(class_names[new_train_labels[i][0]]) 

plt.show() 

model = models.Sequential() 

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) 

model.add(layers.MaxPooling2D((2, 2))) 

model.add(layers.Conv2D(64, (3, 3), activation='relu')) 

model.add(layers.MaxPooling2D((2, 2))) 

model.add(layers.Conv2D(64, (3, 3), activation='relu')) 

model.add(layers.Flatten()) 

model.add(layers.Dense(64, activation='relu')) 

model.add(layers.Dense(10)) 

model.compile(optimizer='adam', 

              loss=tf.keras.losses.sparseCategoricalCrossentropy(from_logits=True), 

              metrics=['accuracy']) 

 
 

history = model.fit(new_train_images, new_train_labels, epochs=10,  

                    validation_data=(new_test_images, new_test_labels)) 

 

plt.plot(history.history['accuracy'], label='accuracy') 

plt.plot(history.history['val_accuracy'], label = 'val_accuracy') 

plt.xlabel('Epoch') 

plt.ylabel('Accuracy') 

plt.ylim([0.5, 1]) 

plt.legend(loc='lower right') 

 
 

test_loss, test_acc = model.evaluate(new_test_images,  new_test_labels, verbose=2)

Here is the implementation of the following given code

Here is the Solution to this error.

In this example we will use the tf.keras.losses.SparseCategoricalCrossentropy() function.

Syntax:

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

• It consists of a few parameters
  • from_logits: whether a logits tensor is expected for y pred. We presume that y pred by default encodes a probability distribution and by default, it takes a false value.
  • reduction: By default it takes losses_utils.ReductionV2.AUTO.

Example:

import tensorflow as tf 

 
from tensorflow.keras import datasets, layers, models 

import matplotlib.pyplot as plt 

(new_train_images, new_train_labels), (new_test_images, new_test_labels) = datasets.cifar10.load_data() 

 
 

new_train_images, new_test_images = new_train_images / 255.0, new_test_images / 255.0 

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 

               'dog', 'frog', 'horse', 'ship', 'truck'] 

 
plt.figure(figsize=(10,10)) 

for i in range(25): 

    plt.subplot(5,5,i+1) 

    plt.xticks([]) 

    plt.yticks([]) 

    plt.grid(False) 

    plt.imshow(new_train_images[i]) 

    plt.xlabel(class_names[new_train_labels[i][0]]) 

plt.show() 

model = models.Sequential() 

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) 

model.add(layers.MaxPooling2D((2, 2))) 

model.add(layers.Conv2D(64, (3, 3), activation='relu')) 

model.add(layers.MaxPooling2D((2, 2))) 

model.add(layers.Conv2D(64, (3, 3), activation='relu')) 

model.add(layers.Flatten()) 

model.add(layers.Dense(64, activation='relu')) 

model.add(layers.Dense(10)) 

model.compile(optimizer='adam', 

              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), 

              metrics=['accuracy']) 

 
 

history = model.fit(new_train_images, new_train_labels, epochs=10,  

                    validation_data=(new_test_images, new_test_labels)) 

 

plt.plot(history.history['accuracy'], label='accuracy') 

plt.plot(history.history['val_accuracy'], label = 'val_accuracy') 

plt.xlabel('Epoch') 

plt.ylabel('Accuracy') 

plt.ylim([0.5, 1]) 

plt.legend(loc='lower right') 

 
 

test_loss, test_acc = model.evaluate(new_test_images,  new_test_labels, verbose=2) 

Here is the Screenshot of the following given code

Also, take a look at some more TensorFlow tutorials in Python.

• TensorFlow next_batch + Examples
• Tensorflow convert sparse tensor to tensor
• TensorFlow global average pooling
• Binary Cross Entropy TensorFlow
• Tensorflow embedding_lookup
• Module ‘tensorflow’ has no attribute ‘optimizers’
• Module ‘tensorflow’ has no attribute ‘sparse_placeholder’

In this Python tutorial, we have focused on how to fix the attributeerror: module ‘tensorflow’ has no attribute ‘sparse_placeholder’ in our model, and also we will look at some examples of how we can use the tf.sparse_placeholder() function in TensorFlow. And we have covered these topics.

• Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_placeholder’
• Attributeerror: module ‘tensorflow’ has no attribute ‘sparsetensorvalue’
• Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_to_dense’
• Attributeerror: module ‘tensorflow’ has no attribute ‘sparse’
• Attributeerror: module ‘tensorflow.keras.losses’ has no attribute ‘sparsecategoricalcrossentropy’