When training **Python Keras** networks using optimizers like stochastic gradient descent (SGD), the learning rate of the network stays constant throughout the training process. This will work in many scenarios. But as we get closer to optima, reducing a learning rate a bit over time can help get better results. It can boost the performance of the model. There are various ways to reduce the learning rate over time during the training process. It's commonly referred to as **learning rate scheduling** or **learning rate annealing**. Keras provides many learning rate schedulers that we can use to anneal the learning rate over time.

As a part of this tutorial, we'll discuss various learning rate schedulers available from keras as well as, we'll explain how one can implement a custom scheduler if existing schedulers do not satisfy their requirements. We have used the Fashion MNIST dataset for our tutorial and have trained a simple convolutional neural network on it to explain various schedulers.

Below, we have highlighted important sections of tutorial to give an overview of the material covered.

- Load Fashion MNIST Dataset
- Define CNN Model
- Train Network With Different Schedulers

Below, we have loaded keras and printed the version of it that we'll use in our tutorial.

```
import tensorflow as tf
from tensorflow import keras
print("Keras Version : {}".format(keras.__version__))
```

In this section, we have loaded the Fashion MNIST dataset available from the keras **detasets** module. The dataset has grayscale images of size **(28,28)** pixels for 10 different fashion items. The dataset is already divided into the train (60k images) and test (10k images) sets. The below table shows the mapping from index to fashion item names.

Label | Description |
---|---|

0 | T-shirt/top |

1 | Trouser |

2 | Pullover |

3 | Dress |

4 | Coat |

5 | Sandal |

6 | Shirt |

7 | Sneaker |

8 | Bag |

9 | Ankle boot |

```
from tensorflow.keras import datasets
import numpy as np
from tensorflow.keras.utils import to_categorical
(X_train, Y_train), (X_test, Y_test) = datasets.fashion_mnist.load_data()
X_train,X_test = X_train.reshape(-1,28,28,1), X_test.reshape(-1,28,28,1)
Y_train, Y_test = to_categorical(Y_train), to_categorical(Y_test)
classes = np.unique(Y_train)
X_train.shape, X_test.shape, Y_train.shape, Y_test.shape
```

In this section, we have defined the CNN that we'll be using for our classification task when explaining various learning rate schedules. We have created a simple function that will create a neural network and return it each time it is called. The neural network has the simple architecture of 2 convolution layers followed by one dense layer. The convolution layers have filters of sizes **32** and **16** respectively. Both convolution layers will apply kernels of shape **(3,3)** on input image data. We have applied **relu (rectified linear unit)** activation after each convolution layer application. Then, we have flattened the output of the second convolution layer and has filled it into a dense layer that has 10 output units (same as a number of classes). The output of the dense layer has been converted to probabilities by applying **softmax** activation function.

```
from tensorflow.keras import Sequential
from tensorflow.keras import layers
def create_model():
return Sequential([
layers.Conv2D(filters=32, kernel_size=(3,3), padding="same", activation="relu", input_shape=(28,28,1)),
layers.Conv2D(filters=16, kernel_size=(3,3), padding="same", activation="relu"),
layers.Flatten(),
layers.Dense(10, activation="softmax")
])
model = create_model()
model.summary()
```

In this section, we have trained our CNN using SGD which has a constant learning rate. We have set the learning rate to a constant value of **0.001**. We have trained the network for only 5 epochs.

```
from tensorflow.keras.optimizers import SGD
grad_descent = keras.optimizers.SGD(learning_rate=0.001)
model.compile(optimizer=grad_descent, loss="categorical_crossentropy", metrics=["accuracy"])
model.fit(x=X_train, y=Y_train, batch_size=64, epochs=5, validation_data=(X_test,Y_test))
```

In this section, we are training the neural network again with SGB but this time we have provided **decay** rate as well. It'll decay the learning rate by following the below formula.

```
learning_rate = learning_rate / (1. + decay * local_step)
```

In the next cell after training, we have also printed the code that has logic to handle decay in the keras codebase. We have used the python **inspect** module for retrieving code.

```
model = create_model() ##
epochs = 5
lr = 0.001
grad_descent = keras.optimizers.SGD(learning_rate=lr, decay=lr/epochs)
model.compile(optimizer=grad_descent, loss="categorical_crossentropy", metrics=["accuracy"])
model.fit(x=X_train, y=Y_train, batch_size=64, epochs=epochs, validation_data=(X_test,Y_test))
```

```
import inspect
print("====== SGD Source ====================================")
print(inspect.getsource(keras.optimizers.SGD)[:510])
print()
print("====== Decay Learning Rate Method ====================")
print(inspect.getsource(keras.optimizers.Optimizer._decayed_lr))
```

In this section, we have trained our network using SGD with exponential decay. We can create an instance of exponential decay using **ExponentialDecay** constructor available from **keras.optimizers.schedules** module. It has the below-mentioned important parameters.

**initial_learning_rate**- This parameter accepts the initial learning rate of the optimizer.**decay_steps**- Number of steps after which to reduce learning rate. Here, one step refers to the execution of one batch of data.**decay_rate**- The float value specifying decay rate.**staircase**- This parameter accepts boolean value which if set to**True**will follow staircase function.

The decayed learning rate is calculated using the below formula.

```
learning_rate = initial_learning_rate * decay_rate ^ (step / decay_steps)
```

Below, we have created an exponential decay with an initial learning rate of **0.001**, **500** decay steps, and a decay rate of **0.98**. This scheduler will decay the learning rate after every 500 steps/batches. We have provided a scheduler to **SGD**.

In the next cell after the training cell, we have also retrieved the learning rate for 5000 steps and plotted them to give an idea of how the learning rate will change during our training process. In our case, the dataset has **60k** images and we have used **64** samples per batch which will bring a number of steps per epoch to **~1000**. As we are training for 5 epochs, the total steps will be **~5000**.

```
from tensorflow.keras.optimizers.schedules import ExponentialDecay
model = create_model() ## Create Model
epochs = 5
lr = 0.001
lr_schedule = ExponentialDecay(lr, decay_steps=500, decay_rate=0.98, staircase=True)
grad_descent = keras.optimizers.SGD(learning_rate=lr_schedule)
model.compile(optimizer=grad_descent, loss="categorical_crossentropy", metrics=["accuracy"])
model.fit(x=X_train, y=Y_train, batch_size=64, epochs=epochs, validation_data=(X_test,Y_test))
```

```
import matplotlib.pyplot as plt
lrs = [lr_schedule(step) for step in range(5000)]
plt.scatter(range(5000), lrs);
plt.title("ExponentialDecay");
plt.xlabel("Steps")
plt.ylabel("Learning Rate");
```

In this section, we are training our network using SGD with a piecewise constant decay scheduler. We can create an instance of piece-wise constant decay scheduler using **PiecewiseConstantDecay()** constructor available from **keras.optimizers.schedules** module. It has the below-mentioned parameters.

**boundaries**- List of integers specifying boundaries for which learning rate will be constant. This parameter will divide the training process based on a number of steps provided in it and will use the learning rate according to**values**parameter. It'll become clear when we explain it below with an example.**values**- List of learning rate values for boundaries specified using**boundaries**parameter. It'll have one value more than**boundaries**.

In our case, we have set **boundaries** to **[1000,2000,3000]** and **values** to **[0.003,0.002,0.001,0.0001]**. We know that our training process has **~5000** steps as we explained earlier. This assign learning rate of **0.003** to first **1000** steps, learning rate of **0.002** to steps from **1000** to **2000**, learning rate of **0.001** to steps from **2000** to **3000** and learning rate of **0.0001** to steps beyond **3000**.

Later on, in the next cell, we have also displayed a plot showing how the learning rate will change during our training of **~5000** steps.

```
from tensorflow.keras.optimizers.schedules import PiecewiseConstantDecay
model = create_model() ## Create Model
epochs = 5
lr_schedule = PiecewiseConstantDecay(boundaries=[1000, 2000, 3000], values=[0.003,0.002,0.001, 0.0001])
grad_descent = keras.optimizers.SGD(learning_rate=lr_schedule)
model.compile(optimizer=grad_descent, loss="categorical_crossentropy", metrics=["accuracy"])
model.fit(x=X_train, y=Y_train, batch_size=64, epochs=epochs, validation_data=(X_test,Y_test))
```

```
import matplotlib.pyplot as plt
lrs = [lr_schedule(step) for step in range(5000)]
plt.scatter(range(5000), lrs);
plt.title("PiecewiseConstantDecay");
plt.xlabel("Steps")
plt.ylabel("Learning Rate");
```

In this section, we have trained our network using SGD with polynomial decay. We can create an instance of polynomial decay using **PolynomialDecay()** constructor available from **keras.optimizers.schedules** module. It has the below-mentioned parameters.

**initial_learning_rate**- This is the initial learning rate of the training.**decay_steps**- Total number of steps for which to decay learning rate.**end_learning_rate**- Final learning rate below which learning rate should not go.**power**- Float to calculate decay learning rate. If we provide a value less than 1 then the curve of learning rate will be concave else it'll be convex (see below plot).

It uses the below formula to calculate the learning rate at any step.

```
def decayed_learning_rate(step):
step = min(step, decay_steps)
return ((initial_learning_rate - end_learning_rate) *
(1 - step / decay_steps) ^ (power)
) + end_learning_rate
```

In our case, we have used an initial learning rate of **0.005**, an end learning rate of **0.001**, and a power value of **1.5**.

```
from tensorflow.keras.optimizers.schedules import PolynomialDecay
model = create_model() ## Create Model
epochs = 5
lr_schedule = PolynomialDecay(0.003, 5000,0.001, power=1.5)
grad_descent = keras.optimizers.SGD(learning_rate=lr_schedule)
model.compile(optimizer=grad_descent, loss="categorical_crossentropy", metrics=["accuracy"])
model.fit(x=X_train, y=Y_train, batch_size=64, epochs=epochs, validation_data=(X_test,Y_test))
```

```
import matplotlib.pyplot as plt
lrs = [lr_schedule(step) for step in range(5000)]
plt.scatter(range(5000), lrs);
plt.title("PolynomialDecay");
plt.xlabel("Steps")
plt.ylabel("Learning Rate");
```

In this section, we are training our network using SGD with an inverse time decay scheduler. We can create an instance of inverse time decay scheduler using **InverseTimeDecay()** constructor available from **keras.optimizers.schedules** module. It has the below-mentioned important parameters.

**initial_learning_rate****decay_steps**- It's an integer specifying number of steps after which decay learning rate.**decay_rate**- It's a float value specifying decay rate.**staircase**

The below formula is used to calculate the learning rate at any step.

```
def decayed_learning_rate(step):
return initial_learning_rate / (1 + decay_rate * step / decay_step)
```

We have created an inverse decay scheduler with an initial learning rate of **0.003**, decay steps of **100**, and decay rate of **0.5**. We have also plotted how the learning rate will change during the training process in the next cell.

```
from tensorflow.keras.optimizers.schedules import InverseTimeDecay
model = create_model() ## Create Model
epochs = 5
lr_schedule = InverseTimeDecay(0.003, 100, 0.5)
grad_descent = keras.optimizers.SGD(learning_rate=lr_schedule)
model.compile(optimizer=grad_descent, loss="categorical_crossentropy", metrics=["accuracy"])
model.fit(x=X_train, y=Y_train, batch_size=64, epochs=epochs, validation_data=(X_test,Y_test))
```

```
import matplotlib.pyplot as plt
lrs = [lr_schedule(step) for step in range(5000)]
plt.scatter(range(5000), lrs);
plt.title("InverseTimeDecay");
plt.xlabel("Steps")
plt.ylabel("Learning Rate");
```

In this section, we have explained how we can create a learning rate scheduler of our own. In order to create a learning rate scheduler, we need to create a function that takes as input epoch number and current learning rate and then returns a new learning rate. Then, we need to wrap this function inside of **LearningRateScheduler** callback available from **keras.callbacks** module. We can provide this callback to **fit()** method and it'll change the learning rate using this function after each epoch. Please make a **NOTE** that this will change the learning rate after the complete epoch and not for individual steps.

If you want to know about callbacks in keras then please feel free to check the below link.

We have also plotted in the next cell how the learning rate will change over time after each epoch.

```
def custome_lr_scheduler(epoch, current_lr):
return current_lr / 3
```

```
from keras.callbacks import LearningRateScheduler
from tensorflow.keras.optimizers.schedules import InverseTimeDecay
model = create_model() ## Create Model
epochs = 5
lr_schedule = LearningRateScheduler(custome_lr_scheduler)
grad_descent = keras.optimizers.SGD(learning_rate=0.001)
model.compile(optimizer=grad_descent, loss="categorical_crossentropy", metrics=["accuracy"])
model.fit(x=X_train, y=Y_train, batch_size=64, epochs=epochs, validation_data=(X_test,Y_test), callbacks=[lr_schedule])
```

```
import matplotlib.pyplot as plt
current_lr = 0.001
lrs = [current_lr]
for epoch in range(1,5):
current_lr = custome_lr_scheduler(epoch, current_lr)
lrs.append(current_lr)
plt.scatter(range(5), lrs);
plt.title("Custome Learning Rate Schedule");
plt.xlabel("Epochs")
plt.ylabel("Learning Rate");
```

This ends our small tutorial explaining how we can use learning rate schedulers available from keras to anneal learning rate during the training process. We also explained how we can create our own custom callback. Please feel free to let us know your views in the comments section.

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