Module ‘tensorflow’ has no attribute ‘optimizers’

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

• Attributeerror module ‘tensorflow’ has no attribute ‘optimizers’
• Attributeerror module ‘tensorflow.Keras.optimizers’ has no attribute ‘rmsprop’
• Attributeerror module ‘tensorflow.Keras.optimizers’ has no attribute ‘adam’
• Attributeerror module ‘tensorflow.Keras.optimizers’ has no attribute ‘experimental’
• Attributeerror module ‘tensorflow.Keras.optimizers’ has no attribute ‘rectified adam’
• Attributeerror module ‘tensorflow.Keras.optimizers.schedules’ has no attribute ‘cosine_decay’
• Attributeerror module ‘tensorflow.python.Keras.optimizers’ has no attribute ‘sgd’

Attributeerror module ‘tensorflow’ has no attribute ‘Optimizers’

• In order to minimize the loss function and increase model accuracy more quickly, optimizers are methods or algorithms that reduce a loss (a type of error) by adjusting various parameters and weights.
• Optimizers are a subclass of extended classes that contain extra data for training a particular model. It’s important to keep in mind that no Tensor is required the optimizer class is initialized with the provided parameters. The optimizers are working to increase training a particular model’s performance and speed.

Example:

import tensorflow as tf 

tf.compat.v1.disable_eager_execution() 

new_tens = tf.Variable(23, name = 'new_tens', dtype = tf.float32) 

new_val = tf.compat.v1.log(new_tens) 

new_log = tf.compat.v1.square(new_val) 

optimizer = tf.Optimizer(0.5) 

train = optimizer.minimize(new_log ) 

init = tf.compat.v1.initialize_all_variables() 

def optimize(): 

   with tf.compat.v1.Session() as session: 

      session.run(init) 

      print("x:", session.run(new_tens), session.run(new_log )) 

       

      for step in range(10): 

         session.run(train) 

         print("step", step, "x:", session.run(new_tens), session.run(new_log )) 

optimize() 

Here is the execution of the following given code

Here is the Solution to this error.

In this example, we are going to use tf.compat.v1.train.GradientDescentOptimizer() function and this function optimizer that executes the gradient descent algorithm.

Syntax:

tf.compat.v1.train.GradientDescentOptimizer(
    learning_rate, use_locking=False, name='GradientDescent'
)

• It consists of a few parameters
  • learning_rate: This parameter defines the floating point value and it is the learning rate to use.
  • use_locking: It will check the condition if it is true then use locks for the update operation.
  • name: By default, it takes the ‘GradientDescent’ value and specifies the name of the operation.

import tensorflow as tf 

tf.compat.v1.disable_eager_execution() 

new_tens = tf.Variable(23, name = 'new_tens', dtype = tf.float32) 

new_val = tf.compat.v1.log(new_tens) 

new_log = tf.compat.v1.square(new_val) 

optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.5) 

train = optimizer.minimize(new_log ) 

init = tf.compat.v1.initialize_all_variables() 

 
def optimize(): 

   with tf.compat.v1.Session() as session: 

      session.run(init) 

      print("x:", session.run(new_tens), session.run(new_log )) 

       

      for step in range(10): 

         session.run(train) 

         print("step", step, "x:", session.run(new_tens), session.run(new_log )) 

optimize() 

You can refer to the below Screenshot

This is how we can solve the attributeerror module ‘tensorflow’ has no attribute ‘optimizers’.

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

Attributeerror module ‘tensorflow.Keras.optimizers’ has no attribute ‘rmsprop’

• In this section, we will discuss how to solve the attributeerror module tensorflow.Keras.optimizers have no attribute ‘rmsprop’.
• In order to train neural networks, RMSprop is a gradient-based optimization method. As data passes through extremely complicated processes like neural networks, gradients have a propensity to either disappear or expand (refer to the vanishing gradients problem). A stochastic method for mini-batch learning called Rmsprop was developed.
• One of the most well-liked optimizers among fans of deep learning is MS prop. This may be the case because, although not having been published, it is nonetheless well-known in the neighborhood.

Example:

from keras import layers
from keras import models
from tensorflow.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=optimizers.RMSprop(lr=1e-4), metrics=['acc'])

Here is the implementation of the following given code

The solution to this error.

In this example, we are going to use the tf.optimizers.RMSprop() function.

Syntax:

tf.keras.optimizers.RMSprop(
    learning_rate=0.001, rho=0.9, momentum=0.0, epsilon=1e-07, centered=False,
    name='RMSprop', **kwargs
)

Example:

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))

Here is the Implementation of the following given code.

As you can see in the Screenshot we have solved the attributeerror module tensorflow.Keras.optimizers has no attribute rmsprop.

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

Attributeerror module ‘tensorflow’ has no attribute ‘adam’

• Adam is one of the most popular optimization methods currently in use. This approach determines the adaptive learning rate for each parameter.
• This approach combines the benefits of momentum and RMSprop. i.e., stores the decaying average of previously squared gradients as well as previous gradients.

Example:

import tensorflow as tf
new_optimizer = tf.Adam(learning_rate=0.1)
new_var = tf.Variable(10.0)
loss = lambda: (new_var ** 3)/2.0   
step_count = new_optimizer.minimize(loss, [new_var]).numpy()
new_var.numpy()

Here is the Screenshot of the following given code

The solution to this error.

In this example, we are going to use the tf.Keras.optimizers.Adam() and this function optimizes the algorithm.

Syntax:

tf.keras.optimizers.Adam(
    learning_rate=0.001,
    beta_1=0.9,
    beta_2=0.999,
    epsilon=1e-07,
    amsgrad=False,
    name='Adam',
    **kwargs
)

• It consists of a few parameters
  • learning_rate= a schedule that is a tf.keras.optimizers.schedules, a floating point value, or a tensor. LearningRateSchedule or a callable that returns the actual value to use with no parameters accepted the velocity of the learning standard value is 0.001.
  • beta_1: An actual value to use, a float value, a constant float tensor, or a callable that accepts no parameters. the first moment’s estimated exponential decline rate. by default, 0.9.
  • amsgrad: By default, it takes the false value and it will apply the amsgrad variant of the algorithm.
  • name: This parameter specifies the name of the operation and by default, it takes ‘Adam’.

import tensorflow as tf

new_optimizer = tf.keras.optimizers.Adam(learning_rate=0.1)
new_var = tf.Variable(10.0)
loss = lambda: (new_var ** 3)/2.0   
step_count = new_optimizer.minimize(loss, [new_var]).numpy()
new_var.numpy()

You can refer to the below Screenshot.

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

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

Attributeerror module ‘tensorflow.Keras.optimizers’ has no attribute ‘Experimental’

• It shows that the class or method in question is unfinished, in the early stages of development, or, less frequently, not up to standards.
• It is a collection of user contributions that are still available as open-source for testing and feedback even though they haven’t been fully integrated with the core TensorFlow.

Syntax:

Here is the Syntax of tf.keras.optimizers.experimental.Optimizer() function

tf.keras.optimizers.experimental.Optimizer(
    name,
    clipnorm=None,
    clipvalue=None,
    global_clipnorm=None,
    use_ema=False,
    ema_momentum=0.99,
    ema_overwrite_frequency=None,
    jit_compile=True,
    **kwargs
)

• It consists of a few parameters
  • name: the term given to weights for momentum accumulators that the optimizer produced.
  • clipnorm: Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value.
  • clipvalue: If set, the gradient of each weight is clipped to be no higher than this value.

Example:

import tensorflow as tf 
new_optimizer = tf.keras.optimizers.Experimental.SGD(learning_rate=1, clipvalue=1)
var1, var2 = tf.Variable(2.0), tf.Variable(2.0)
with tf.GradientTape() as tape:
  loss = 2 * var1 + 2 * var2
grads = tape.gradient(loss, [var1, var2])
print([grads[0].numpy(), grads[1].numpy()])

You can refer to the below Screenshot

As you can see in the Screenshot we have solved the attributeerror module tensorflow.Keras.optimizers has no attribute Experimental.

Read: TensorFlow Fully Connected Layer

Attributeerror module ‘tensorflow.addons.optimizers’ has no attribute ‘rectified adam’

• In this section, we will discuss how to solve the attributeerror module tensorflow.Keras.optimizers’ has no attribute ‘rectified adam’.
• Rectified Adam, often known as RAdam, is a stochastic optimizer variation that adds a term to correct the adaptive learning rate’s variance. It attempts to solve Adam’s terrible convergence issue.

Example:

import tensorflow as tf
import tensorflow_addons as tfa
new_random = tfa.optimizers.rectifiedAdam()
new_output= tfa.optimizers.Lookahead(new_random, sync_period=6, slow_step_size=0.5)
print(new_output)

Here is the implementation of the following given code

A solution to this error

In this example, we are going to use the tensorflow.addons.optimizers() function

Syntax:

Here is the Syntax of tensorflow.addons.optimizers() function in Python TensorFlow

tfa.optimizers.RectifiedAdam(
    learning_rate: Union[FloatTensorLike, Callable, Dict] = 0.001,
    beta_1: tfa.types.FloatTensorLike = 0.9,
    beta_2: tfa.types.FloatTensorLike = 0.999,
    epsilon: tfa.types.FloatTensorLike = 1e-07,
    weight_decay: Union[FloatTensorLike, Callable, Dict] = 0.0,
    amsgrad: bool = False,
    sma_threshold: tfa.types.FloatTensorLike = 5.0,
    total_steps: int = 0,
    warmup_proportion: tfa.types.FloatTensorLike = 0.1,
    min_lr: tfa.types.FloatTensorLike = 0.0,
    name: str = 'RectifiedAdam',
    **kwargs
)

• It consists of a few parameters
  • learning_rate: It is a tensor or floating point value.
  • beta_1: a float quantity or a fixed float tensor. estimations for the first moment’s exponential decline rate.
  • beta_2: a float quantity or a fixed float tensor. estimations of the second moment’s exponential decline rate.
  • weight_decay: A tensor or a floating value or a schedule that is a weight decay for each argument.
  • amsgrad: Whether to apply the amsgrad variant of the algorithm then threshold simply calculates the mean average value.
  • warmup_proportion: It is a floating point value.
  • min_lr: It is a floating point value and the minimum learning value.

import tensorflow as tf
import tensorflow_addons as tfa
new_random = tfa.optimizers.RectifiedAdam()
new_output= tfa.optimizers.Lookahead(new_random, sync_period=6, slow_step_size=0.5)
print(new_output)

Here is the implementation of the following given code.

This is how we can solve the attributeerror module tensorflow.addons.optimizers has no rectified adam.

Read: Batch Normalization TensorFlow

Attributeerror module ‘tensorflow’ has no attribute ‘cosine_decay’

• In this section, we will discuss how to solve the attributeerror module ‘tensorflow’ has no attribute ‘cosine_decay’.
• It is frequently advised to lower the learning rate as the training of a model advances. When given an initial learning rate, this function applies a cosine decay function.
• To calculate the decaying learning rate, a global step value is needed. A TensorFlow variable that you increment at each training step can be used instead.

Example:

import tensorflow as tf
result= tf.cosine_decay(learning_rate=0.01,decay_steps=6.5,global_step=12.8)
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.train.cosine_decay() function and this function applies cosine decay to the learning rate.

Syntax:

Here is the Syntax of tf.compat.v1.train.cosine_decay() function

tf.compat.v1.train.cosine_decay(
    learning_rate, global_step, decay_steps, alpha=0.0, name=None
)

• It consits of a few parameters
  • learning_rate: a Python integer, a scalar float32 or float64 Tensor, etc. the speed of initial learning.
  • global_step: a Python number, scalar int32 or int64, or a Tensor. To compute the decay, use a global step.
  • decay_steps: a Python number, scalar int32 or int64, or a Tensor. The number of decaying stages.
  • alpha: By default it takes 0.0 value and it specifies the Minimum learning rate value as a fraction of learning_rate.

import tensorflow as tf
result= tf.compat.v1.train.cosine_decay(learning_rate=0.01,decay_steps=6.5,global_step=12.8)
print(result)

You can refer to the below Screenshot

Read: TensorFlow feed_dict + 9 Examples

Attributeerror module ‘tensorflow.python.Keras.optimizers’ has no attribute ‘sgd’

• Here we will discuss how to solve the attributeerror module ‘tensorflow.python.Keras.optimizers’ has no attribute ‘sgd’.
• The term “stochastic” refers to a process or system connected to a random probability. As a result, in stochastic gradient descent, a small number of samples rather than the entire data set are chosen at random for each iteration.
• The number of samples from a dataset that are used to calculate the gradient for each iteration is referred to in Gradient Descent as the “batch,” which is the plural form of the phrase. The batch in a conventional Gradient Descent optimization, such as Batch Gradient Descent, is assumed to be the entire dataset.

Example:

from keras import layers
from keras import models
from tensorflow.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=optimizers.sgd(lr=1e-4), metrics=['acc'])

Here is the implementation of the following given code

The Solution to this error

from keras import layers
from keras import models
from tensorflow.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=optimizers.sgd(lr=1e-4), metrics=['acc'])

You can refer to the below Screenshot

This is how we can solve the attributerror module ‘tensorflow.python.Keras.optimizers’ has no attribute ‘sgd’.

Also read the following TendorFlow tutorials in Python.

• TensorFlow Sparse Tensor + Examples
• Binary Cross Entropy TensorFlow
• Gradient descent optimizer TensorFlow
• Convert list to tensor TensorFlow
• Module ‘tensorflow’ has no attribute ‘Function’

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

• Attributeerror module ‘tensorflow’ has no attribute ‘optimizers’
• Attributeerror module ‘tensorflow.Keras.optimizers’ has no attribute ‘rmsprop’
• Attributeerror module ‘tensorflow.Keras.optimizers’ has no attribute ‘adam’
• Attributeerror module ‘tensorflow.Keras.optimizers’ has no attribute ‘experimental’
• Attributeerror module ‘tensorflow.Keras.optimizers’ has no attribute ‘rectified adam’
• Attributeerror module ‘tensorflow.Keras.optimizers.schedules’ has no attribute ‘cosine_decay’
• Attributeerror module ‘tensorflow.python.Keras.optimizers’ has no attribute ‘sgd’