Modulenotfounderror no module named tensorflow Keras

In this Python tutorial, we will discuss the error “modulenotfounderror no module named TensorFlow Keras“. And we’ll also cover the following topics:

• Modulenotfounderror no module named tensorflow Keras
• Modulenotfounderror no module named ‘tensorflow.keras.layers.experimental.preprocessing’
• Modulenotfounderror no module named ‘tensorflow.keras.engine’
• Modulenotfounderror no module named ‘tensorflow.keras.layers.merge’
• Modulenotfounderror no module named ‘tensorflow.keras.utils.vis_utils’
• Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’
• Modulenotfounderror no module named ‘tensorflow.keras.layers.embeddings’
• Modulenotfounderror no module named ‘TensorFlow.keras.applications.resnet’
• Modulenotfounderror no module named ‘tensorflow.keras.layers.recurrent

Modulenotfounderror no module named tensorflow Keras

• Here we are going to discuss the module not found error no module named TensorFlow.Keras.
• You can create, fit, test, and apply deep learning models to generate predictions using tf.keras(). Common deep learning tasks, such as classification, and regression predictive modeling, are now approachable for typical developers who just want to get something done.

Example:

Let’s take an example and check how we can solve the module not found error in the module named TensorFlow.Keras.

Source Code

import tensorflow as tf
from tensorflow import keras
from tensorflow.Keras import layers

Here is the Screenshot of the following given code.

As you can see in the Screenshot the output displays the error ModuleNotFoundError: No module named tensorFlow.Keras.

Reason: This error is because there is no module tensorflow.keras and it does not work in any version of tensorflow.

Now let’s see the solution to this.

Solution:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

new_model = keras.Sequential(
    [
        layers.Dense(3, activation="relu", name="layer1"),
        layers.Dense(1, activation="relu", name="layer2"),
        layers.Dense(2, name="layer3"),
    ]
)
tens_1 = tf.ones((3, 3))
tens_2 = new_model(tens_1)
new_model.summary()

In the following given code first, we imported the tensorflow library and keras layers. Next, we created a model by using the keras.Sequential() and within this, we mention the dense layers with activation function ‘relu’. Next, we will display the summary of the model.

You can refer to the below Screenshot.

This is how we can solve the error modulenotfounderror no module named tensorflow Keras in TensorFlow.

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

Modulenotfounderror no module named ‘tensorflow.keras.engine’

• In this section, we will discuss the attribute error modulenotfounderror no module named ‘tensorflow.Keras.engine’ in TensorFlow.
• Keras engines are used it reduces the amount of user interaction needed for typical use scenarios, and it offers clear and responsive error notifications.

Example:

import tensorflow.keras.layers as KL
import tensorflow.keras.models as KM
import tensorflow.keras.engine as KE

Here is the implementation of the following given code

As you can see in the Screenshot the output displays the error ModuleNotFoundError: No module named tensorFlow.Keras.engine.

Reason: This error is because there is no module tensorflow.keras.engine and it does not work in the latest version of tensorflow.

Now let’s see the solution to this.

Solution:

import tensorflow.keras.layers as KL
import tensorflow.keras.models as KM
from tensorflow.keras.layers import Layer, InputSpec

result= tf.keras.layers.InputSpec(dtype=None, shape=(2,2),ndim=None)
print(result)

In the following given code, we have imported the new module of ‘tensorflow.Keras.engine’ that is from tensorflow.keras.layers import Layer, InputSpec and this is an updated version in tensorflow.

In this example we have discussed how to solve the error modulenotfound error no module named tensorflow.keras_.engine.

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

Modulenotfounderror no module named ‘tensorflow.keras.layers.merge’

• Here we are going to discuss the modulenotfounderror no module named ‘tensorFlow.Keras.layers.merge’ in Tensorflow.
• The process of merging layers in an image is often known as “flattening” the image. In an image, you can decide whether to merge all or just some of the layers. The memory requirements for the image are reduced when layers are combined.

Example:

from keras.layers.merge import concatenate

Here is the Screenshot of the following given code.

As you can see in the Screenshot the output displays the modulenotfounderror no module named tensorflow.keras_.layers.merge.

Reason: This error is because there is no module keras.layers.merge and it does not work in the latest version of tensorflow.

The solution to this error

In this example we will use the tf.keras.layers.concatenate() function concatenation of two input layers.

Syntax:

Let’s look at the syntax and understand how we can use the tf.keras.layers.concatenate() function in TensorFlow.

tf.keras.layers.concatenate(
    inputs, axis=-1, **kwargs
)

• It consists of a few parameters
  • inputs: This parameter defines the list of input tensors.
  • axis: By default, it takes a -1 value and it defines the concatenation axis.
  • **kwargs: This parameter indicates the standard layer keyword parameters.

Example:

import tensorflow as tf
import numpy as np
from keras.layers import concatenate
USA_based_random_num = np.arange(20).reshape(2, 2, 5)
print(USA_based_random_num )
sample_num = np.arange(20, 30).reshape(2, 1, 5)
print(sample_num)

result= tf.keras.layers.concatenate([USA_based_random_num , sample_num],axis=1)
print(result)

In the above code first, we imported the tensorflow and numpy library. In this example, we defined a variable “USA_based_random_num” and created an array by using the np.arange() function.

Next, we used the tf.keras.layers.concatenate() function and it will return an input tensor, the concatenation of the inputs alongside the axis.

Here is the implementation of the following given code.

This is how we can solve the error Solution of modulenotfounderror no module named tensorflow.keras_.layers.merge_ in TensorFlow.

Read: How to convert dictionary to tensor tensorflow

Modulenotfounderror no module named ‘tensorflow.keras.utils.vis_utils’

• In this example we will discuss how to solve the modulenotfounderror no module named ‘tensorflow.keras.utils.vis_utils’ in TensorFlow.
• Installing Keras with TensorFlow can be done in one of two ways: Install a Python installation like ActivePython that comes with hundreds of well-liked packages, such as Keras and TensorFlow. Installing Keras and TensorFlow simultaneously can be done by using pip.

Example:

from keras.utils.visualize_util import plot

You can refer to the below Screenshot

In the above example, this module does not exist in the tensorflow version.

Here is the Solution to this error

from keras import layers
from keras import models
from keras import optimizers

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
                        input_shape=(150, 150, 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(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.summary()

from keras import optimizers

model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.RMSprop(learning_rate=0.01))

In the following given code first, we have imported the models and optimizers. Next, we created a sequential model in which we mention the first layer conv2d and within this argument, we passed the input value as well as the activation function.

After that, we declared the next layer which is max pooling2d and within this, we passed the input values.

Here is the execution of the following given code

As you can see in the Screenshot we have discussed how to solve the error modulenotfounderror no module named tensorflow.keras_.utils_.vis_utils in TensorFlow.

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

Modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’

• In this section, we will understand how to solve the modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’ in TensorFlow.
• A numpy array (or) vector of integers representing various categories can be transformed into a numpy array (or) matrix with binary values and columns equal to the number of categories in the data using the method to categorical().

Example:

import tensorflow.keras.utils.np_utils

Here is the implementation of the following given code

As you can see in the Screenshot the output displays the modulenotfounderror no module named tensorflow.keras_.utils_.np_utils.

Reason: This error is because there is no module tensorflow.keras_.utils_.np_utils and it does not work in the latest version of tensorflow.

The solution to this error.

In this example, we will use the tf.keras.utils.to_categorical() function and this function is used to convert a vector to a binary class matrix.

Syntax:

tf.keras.utils.to_categorical(
    y, num_classes=None, dtype='float32'
)

• It consists of a few parameters
  • y: integers from 0 to num classes – 1 are contained in an array-like structure that will be transformed into a matrix.
  • num_classes: number of classes combined. If None, this is implied to be max(y) + 1.
  • dtype: By default, it takes float32 and it defines the data type.

import tensorflow as tf 
from tensorflow.keras.utils import to_categorical
USA_based_code = tf.keras.utils.to_categorical([1, 0, 2, 3], num_classes=4)
USA_based_code = tf.constant(USA_based_code, shape=[4, 4])
print(USA_based_code)

In the following given code first, we imported the tensorflow.keras.utils import to_categorical and then declared a variable “USA_based_code” and used the tf.keras.utils.to_categorical() function.

Here is the Screenshot of the following given code.

This is how we can solve the error modulenotfounderror no module named tensorflow.keras_.utils_.np_utils in TensorFlow.

Read: Tensorflow convert sparse tensor to tensor

Modulenotfounderror no module named ‘tensorflow.keras.layers.embeddings’

• In this example we will discuss the error modulenotfounderror no module named ‘tensorflow.keras.layers.embeddings’ in TensorFlow.
• A dense vector of floating point values is an embedding (the length of the vector is a parameter you specify). The embedding values are trainable parameters rather than being manually specified (weights learned by the model during training, in the same way, a model learns weights for a dense layer).

Example:

from tensorflow.keras.layers.embeddings import Embedding

Here is the implementation of the following given code

The Solution to this error.

In this example, we will use the tf.keras.layers.Embedding() function and this function is used to convert the positive number indexes into dense vectors.

Syntax:

tf.keras.layers.Embedding(
    input_dim,
    output_dim,
    embeddings_initializer='uniform',
    embeddings_regularizer=None,
    activity_regularizer=None,
    embeddings_constraint=None,
    mask_zero=False,
    input_length=None,
    **kwargs
)

• It consists of a few parameters
  • input_dim: Size of the vocabulary expressed as the maximum integer index +1.
  • output_dim: This parameter defines the integer value and it represents the dimensions of the dense embedding.
  • embeddings_initializer: This is an initializer for the embedding matrix.
  • mask_zero: Boolean, indicating whether or not the input value 0 is a unique “padding” value that has to be hidden. This helps utilize recurrent layers, which may accept input of varying lengths.
  • If this is accurate, an exception will be thrown and all subsequent model layers will need to support masking. If mask_zero is set to True, index 0 cannot be used in the vocabulary (input dim should be equal to the vocabulary’s size plus 1).

Example:

import tensorflow as tf
import numpy as np
new_model = tf.keras.Sequential()
new_model.add(tf.keras.layers.Embedding(1000, 64,input_length=10))

random_num = np.random.randint(1000, size=(32, 10))
new_model.compile('rmsprop', 'mse')
new_result = new_model.predict(random_num )
print(new_result.shape)

You can refer to the below Screenshot

As you can see in the Screenshot we have discussed how to solve the error modulenotfounderror no module named tensorflow.keras_.layers.embeddings in TensorFlow.

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

Modulenotfounderror no module named ‘TensorFlow.keras.applications.resnet’

• Here we are going to discuss how to solve the error modulenotfounderror no module named ‘TensorFlow.keras.applications.resnet’ in TensorFlow.
• For applications like image recognition, convolutional neural networks (CNNs) with ResNet are used.

Example:

Let’s take an example and check how to solve the error modulenotfounderror no module named ‘TensorFlow.keras.applications.resnet’ in TensorFlow.

Source Code:

import tensorflow.keras.applications.ResNet

Here is the implementation of the following given code.

Solution:

Here is the Solution to this error

import keras
import keras.applications.resnet
from keras.applications.resnet import ResNet50
model = ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)
model.summary()

Here is the implementation of the following given code

In this example we have discussed the error modulenotfounderror no module named TensorFlow.keras_.applications.resnet in TensorFlow.

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

Modulenotfounderror no module named ‘tensorflow.keras.layers.recurrent

In this section, we will discuss how to solve the error modulenotfounderror no module named ‘tensorflow.keras.layers.recurrent in TensorFlow.

Example:

from tensorflow.keras.layers.recurrent import Sequential

Here is the implementation of the following given code.

As you can see in the Screenshot the output displays the error modulenotfounderror no module named ‘tensorflow.keras.layers.recurrent

Reason: This error is because there is no module tensorflow.keras.layers.recurrent and it does not work in any version of tensorflow.

Now let’s see the solution to this

Solution:

import numpy as np
import tensorflow as tf
from tensorflow.keras import Input, Model
from tensorflow.keras.layers import Dense, Flatten, Conv2D

np.random.seed(44)
tf.random.set_seed(44)


new_arr = np.random.rand(4, 32, 32, 3)

input_shape = Input(shape=new_arr.shape[1:])
convolution_layer = Conv2D(filters=8, kernel_size=(3, 3), activation='relu')(input_shape)
flatten = Flatten()(convolution_layer)
feature_map = Dense(8, activation='relu')(flatten)
new_output = Dense(2, activation='softmax')(flatten)
result = Model(inputs=input_shape, outputs=new_output)

result(new_arr)

In the above code, we have imported the numpy and TensorFlow libraries. Next, we used the tf.random.set_seed() function. The global and operation-level seeds are the source of the random seed used by operations.

When the global seed is pre-determined but the operation seed is not, the system deterministically chooses an operation seed in addition to the global seed to produce a distinct random sequence. Next, I used the conv2d() layer and assign filters with kernel_size().

After that, we add the dense layer with input shape 8 and the activation function ‘relu’.

Here is the execution of the following given code

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

• Module ‘tensorflow’ has no attribute ‘log’
• TensorFlow Fully Connected Layer
• Batch Normalization TensorFlow
• TensorFlow feed_dict + 9 Examples

So, in this Python tutorial, we have discussed the error “modulenotfounderror no module named TensorFlow Keras“. And also we covered the following topics:

• modulenotfounderror no module named ‘tensorflow.keras.layers.experimental.preprocessing’
• modulenotfounderror no module named ‘tensorflow.keras.engine’
• modulenotfounderror no module named ‘tensorflow.keras.layers.merge’
• modulenotfounderror no module named ‘tensorflow.keras.utils.vis_utils’
• modulenotfounderror no module named ‘tensorflow.keras.utils.np_utils’
• modulenotfounderror no module named ‘tensorflow.keras.layers.embeddings’
• modulenotfounderror no module named ‘TensorFlow.keras.applications.resnet’
• modulenotfounderror no module named ‘tensorflow.keras.layers.recurrent