Plotting the Training and Validation Loss Curves for the Transformer Model

By Stefania Cristina on October 19, 2022 in Attention

Last Updated on January 6, 2023

We have previously seen how to train the Transformer model for neural machine translation. Before moving on to inferencing the trained model, let us first explore how to modify the training code slightly to be able to plot the training and validation loss curves that can be generated during the learning process.

The training and validation loss values provide important information because they give us a better insight into how the learning performance changes over the number of epochs and help us diagnose any problems with learning that can lead to an underfit or an overfit model. They will also inform us about the epoch with which to use the trained model weights at the inferencing stage.

In this tutorial, you will discover how to plot the training and validation loss curves for the Transformer model.

After completing this tutorial, you will know:

How to modify the training code to include validation and test splits, in addition to a training split of the dataset
How to modify the training code to store the computed training and validation loss values, as well as the trained model weights
How to plot the saved training and validation loss curves

Kick-start your project with my book Building Transformer Models with Attention. It provides self-study tutorials with working code to guide you into building a fully-working transformer model that can
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Plotting the training and validation loss curves for the Transformer model
Photo by Jack Anstey, some rights reserved.

Tutorial Overview

This tutorial is divided into four parts; they are:

Recap of the Transformer Architecture
Preparing the Training, Validation, and Testing Splits of the Dataset
Training the Transformer Model
Plotting the Training and Validation Loss Curves

Prerequisites

For this tutorial, we assume that you are already familiar with:

Recap of the Transformer Architecture

Recall having seen that the Transformer architecture follows an encoder-decoder structure. The encoder, on the left-hand side, is tasked with mapping an input sequence to a sequence of continuous representations; the decoder, on the right-hand side, receives the output of the encoder together with the decoder output at the previous time step to generate an output sequence.

The encoder-decoder structure of the Transformer architecture
Taken from “Attention Is All You Need“

In generating an output sequence, the Transformer does not rely on recurrence and convolutions.

You have seen how to train the complete Transformer model, and you shall now see how to generate and plot the training and validation loss values that will help you diagnose the model’s learning performance.

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Preparing the Training, Validation, and Testing Splits of the Dataset

In order to be able to include validation and test splits of the data, you will modify the code that prepares the dataset by introducing the following lines of code, which:

Specify the size of the validation data split. This, in turn, determines the size of the training and test splits of the data, which we will be dividing into a ratio of 80:10:10 for the training, validation, and test sets, respectively:

self.val_split = 0.1  # Ratio of the validation data split

1	self.val_split = 0.1 # Ratio of the validation data split

Split the dataset into validation and test sets in addition to the training set:

val = dataset[int(self.n_sentences * self.train_split):int(self.n_sentences * (1-self.val_split))]
test = dataset[int(self.n_sentences * (1 - self.val_split)):]

1 2	val = dataset[int(self.n_sentences * self.train_split):int(self.n_sentences * (1-self.val_split))] test = dataset[int(self.n_sentences * (1 - self.val_split)):]

Prepare the validation data by tokenizing, padding, and converting to a tensor. For this purpose, you will collect these operations into a function called encode_pad, as shown in the complete code listing below. This will avoid excessive repetition of code when performing these operations on the training data as well:

valX = self.encode_pad(val[:, 0], enc_tokenizer, enc_seq_length)
valY = self.encode_pad(val[:, 1], dec_tokenizer, dec_seq_length)

1 2	valX = self.encode_pad(val[:, 0], enc_tokenizer, enc_seq_length) valY = self.encode_pad(val[:, 1], dec_tokenizer, dec_seq_length)

Save the encoder and decoder tokenizers into pickle files and the test dataset into a text file to be used later during the inferencing stage:

self.save_tokenizer(enc_tokenizer, 'enc')
self.save_tokenizer(dec_tokenizer, 'dec')
savetxt('test_dataset.txt', test, fmt='%s')

self.save_tokenizer(enc_tokenizer, 'enc')

self.save_tokenizer(dec_tokenizer, 'dec')

savetxt('test_dataset.txt', test, fmt='%s')

The complete code listing is now updated as follows:

from pickle import load, dump, HIGHEST_PROTOCOL
from numpy.random import shuffle
from numpy import savetxt
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from tensorflow import convert_to_tensor, int64

class PrepareDataset:
    def __init__(self, **kwargs):
        super(PrepareDataset, self).__init__(**kwargs)
        self.n_sentences = 15000  # Number of sentences to include in the dataset
        self.train_split = 0.8  # Ratio of the training data split
        self.val_split = 0.1  # Ratio of the validation data split

    # Fit a tokenizer
    def create_tokenizer(self, dataset):
        tokenizer = Tokenizer()
        tokenizer.fit_on_texts(dataset)

        return tokenizer

    def find_seq_length(self, dataset):
        return max(len(seq.split()) for seq in dataset)

    def find_vocab_size(self, tokenizer, dataset):
        tokenizer.fit_on_texts(dataset)

        return len(tokenizer.word_index) + 1

    # Encode and pad the input sequences
    def encode_pad(self, dataset, tokenizer, seq_length):
        x = tokenizer.texts_to_sequences(dataset)
        x = pad_sequences(x, maxlen=seq_length, padding='post')
        x = convert_to_tensor(x, dtype=int64)

        return x

    def save_tokenizer(self, tokenizer, name):
        with open(name + '_tokenizer.pkl', 'wb') as handle:
            dump(tokenizer, handle, protocol=HIGHEST_PROTOCOL)

    def __call__(self, filename, **kwargs):
        # Load a clean dataset
        clean_dataset = load(open(filename, 'rb'))

        # Reduce dataset size
        dataset = clean_dataset[:self.n_sentences, :]

        # Include start and end of string tokens
        for i in range(dataset[:, 0].size):
            dataset[i, 0] = "<START> " + dataset[i, 0] + " <EOS>"
            dataset[i, 1] = "<START> " + dataset[i, 1] + " <EOS>"

        # Random shuffle the dataset
        shuffle(dataset)

        # Split the dataset in training, validation and test sets
        train = dataset[:int(self.n_sentences * self.train_split)]
        val = dataset[int(self.n_sentences * self.train_split):int(self.n_sentences * (1-self.val_split))]
        test = dataset[int(self.n_sentences * (1 - self.val_split)):]

        # Prepare tokenizer for the encoder input
        enc_tokenizer = self.create_tokenizer(dataset[:, 0])
        enc_seq_length = self.find_seq_length(dataset[:, 0])
        enc_vocab_size = self.find_vocab_size(enc_tokenizer, train[:, 0])

        # Prepare tokenizer for the decoder input
        dec_tokenizer = self.create_tokenizer(dataset[:, 1])
        dec_seq_length = self.find_seq_length(dataset[:, 1])
        dec_vocab_size = self.find_vocab_size(dec_tokenizer, train[:, 1])

        # Encode and pad the training input
        trainX = self.encode_pad(train[:, 0], enc_tokenizer, enc_seq_length)
        trainY = self.encode_pad(train[:, 1], dec_tokenizer, dec_seq_length)

        # Encode and pad the validation input
        valX = self.encode_pad(val[:, 0], enc_tokenizer, enc_seq_length)
        valY = self.encode_pad(val[:, 1], dec_tokenizer, dec_seq_length)

        # Save the encoder tokenizer
        self.save_tokenizer(enc_tokenizer, 'enc')

        # Save the decoder tokenizer
        self.save_tokenizer(dec_tokenizer, 'dec')

        # Save the testing dataset into a text file
        savetxt('test_dataset.txt', test, fmt='%s')

        return trainX, trainY, valX, valY, train, val, enc_seq_length, dec_seq_length, enc_vocab_size, dec_vocab_size

from pickle import load, dump, HIGHEST_PROTOCOL

from numpy.random import shuffle

from numpy import savetxt

from keras.preprocessing.text import Tokenizer

from keras.preprocessing.sequence import pad_sequences

from tensorflow import convert_to_tensor, int64

class PrepareDataset:

def __init__(self, **kwargs):

super(PrepareDataset, self).__init__(**kwargs)

self.n_sentences = 15000 # Number of sentences to include in the dataset

self.train_split = 0.8 # Ratio of the training data split

self.val_split = 0.1 # Ratio of the validation data split

# Fit a tokenizer

def create_tokenizer(self, dataset):

tokenizer = Tokenizer()

tokenizer.fit_on_texts(dataset)

return tokenizer

def find_seq_length(self, dataset):

return max(len(seq.split()) for seq in dataset)

def find_vocab_size(self, tokenizer, dataset):

tokenizer.fit_on_texts(dataset)

return len(tokenizer.word_index) + 1

# Encode and pad the input sequences

def encode_pad(self, dataset, tokenizer, seq_length):

x = tokenizer.texts_to_sequences(dataset)

x = pad_sequences(x, maxlen=seq_length, padding='post')

x = convert_to_tensor(x, dtype=int64)

return x

def save_tokenizer(self, tokenizer, name):

with open(name + '_tokenizer.pkl', 'wb') as handle:

dump(tokenizer, handle, protocol=HIGHEST_PROTOCOL)

def __call__(self, filename, **kwargs):

# Load a clean dataset

clean_dataset = load(open(filename, 'rb'))

# Reduce dataset size

dataset = clean_dataset[:self.n_sentences, :]

# Include start and end of string tokens

for i in range(dataset[:, 0].size):

dataset[i, 0] = "<START> " + dataset[i, 0] + " <EOS>"

dataset[i, 1] = "<START> " + dataset[i, 1] + " <EOS>"

# Random shuffle the dataset

shuffle(dataset)

# Split the dataset in training, validation and test sets

train = dataset[:int(self.n_sentences * self.train_split)]

val = dataset[int(self.n_sentences * self.train_split):int(self.n_sentences * (1-self.val_split))]

test = dataset[int(self.n_sentences * (1 - self.val_split)):]

# Prepare tokenizer for the encoder input

enc_tokenizer = self.create_tokenizer(dataset[:, 0])

enc_seq_length = self.find_seq_length(dataset[:, 0])

enc_vocab_size = self.find_vocab_size(enc_tokenizer, train[:, 0])

# Prepare tokenizer for the decoder input

dec_tokenizer = self.create_tokenizer(dataset[:, 1])

dec_seq_length = self.find_seq_length(dataset[:, 1])

dec_vocab_size = self.find_vocab_size(dec_tokenizer, train[:, 1])

# Encode and pad the training input

trainX = self.encode_pad(train[:, 0], enc_tokenizer, enc_seq_length)

trainY = self.encode_pad(train[:, 1], dec_tokenizer, dec_seq_length)

# Encode and pad the validation input

valX = self.encode_pad(val[:, 0], enc_tokenizer, enc_seq_length)

valY = self.encode_pad(val[:, 1], dec_tokenizer, dec_seq_length)

# Save the encoder tokenizer

self.save_tokenizer(enc_tokenizer, 'enc')

# Save the decoder tokenizer

self.save_tokenizer(dec_tokenizer, 'dec')

# Save the testing dataset into a text file

savetxt('test_dataset.txt', test, fmt='%s')

return trainX, trainY, valX, valY, train, val, enc_seq_length, dec_seq_length, enc_vocab_size, dec_vocab_size

Training the Transformer Model

We shall introduce similar modifications to the code that trains the Transformer model to:

Prepare the validation dataset batches:

val_dataset = data.Dataset.from_tensor_slices((valX, valY))
val_dataset = val_dataset.batch(batch_size)

1 2	val_dataset = data.Dataset.from_tensor_slices((valX, valY)) val_dataset = val_dataset.batch(batch_size)

Monitor the validation loss metric:

val_loss = Mean(name='val_loss')

1	val_loss = Mean(name='val_loss')

Initialize dictionaries to store the training and validation losses and eventually store the loss values in the respective dictionaries:

train_loss_dict = {}
val_loss_dict = {}

train_loss_dict[epoch] = train_loss.result()
val_loss_dict[epoch] = val_loss.result()

train_loss_dict = {}

val_loss_dict = {}

train_loss_dict[epoch] = train_loss.result()

val_loss_dict[epoch] = val_loss.result()

Compute the validation loss:

loss = loss_fcn(decoder_output, prediction)
val_loss(loss)

1 2	loss = loss_fcn(decoder_output, prediction) val_loss(loss)

Save the trained model weights at every epoch. You will use these at the inferencing stage to investigate the differences in results that the model produces at different epochs. In practice, it would be more efficient to include a callback method that halts the training process based on the metrics that are being monitored during training and only then save the model weights:

# Save the trained model weights
training_model.save_weights("weights/wghts" + str(epoch + 1) + ".ckpt")

1 2	# Save the trained model weights training_model.save_weights("weights/wghts" + str(epoch + 1) + ".ckpt")

Finally, save the training and validation loss values into pickle files:

with open('./train_loss.pkl', 'wb') as file:
    dump(train_loss_dict, file)

with open('./val_loss.pkl', 'wb') as file:
    dump(val_loss_dict, file)

with open('./train_loss.pkl', 'wb') as file:

dump(train_loss_dict, file)

with open('./val_loss.pkl', 'wb') as file:

dump(val_loss_dict, file)

The modified code listing now becomes:

from tensorflow.keras.optimizers import Adam
from tensorflow.keras.optimizers.schedules import LearningRateSchedule
from tensorflow.keras.metrics import Mean
from tensorflow import data, train, math, reduce_sum, cast, equal, argmax, float32, GradientTape, function
from keras.losses import sparse_categorical_crossentropy
from model import TransformerModel
from prepare_dataset import PrepareDataset
from time import time
from pickle import dump


# Define the model parameters
h = 8  # Number of self-attention heads
d_k = 64  # Dimensionality of the linearly projected queries and keys
d_v = 64  # Dimensionality of the linearly projected values
d_model = 512  # Dimensionality of model layers' outputs
d_ff = 2048  # Dimensionality of the inner fully connected layer
n = 6  # Number of layers in the encoder stack

# Define the training parameters
epochs = 20
batch_size = 64
beta_1 = 0.9
beta_2 = 0.98
epsilon = 1e-9
dropout_rate = 0.1


# Implementing a learning rate scheduler
class LRScheduler(LearningRateSchedule):
    def __init__(self, d_model, warmup_steps=4000, **kwargs):
        super(LRScheduler, self).__init__(**kwargs)

        self.d_model = cast(d_model, float32)
        self.warmup_steps = warmup_steps

    def __call__(self, step_num):

        # Linearly increasing the learning rate for the first warmup_steps, and decreasing it thereafter
        arg1 = step_num ** -0.5
        arg2 = step_num * (self.warmup_steps ** -1.5)

        return (self.d_model ** -0.5) * math.minimum(arg1, arg2)


# Instantiate an Adam optimizer
optimizer = Adam(LRScheduler(d_model), beta_1, beta_2, epsilon)

# Prepare the training dataset
dataset = PrepareDataset()
trainX, trainY, valX, valY, train_orig, val_orig, enc_seq_length, dec_seq_length, enc_vocab_size, dec_vocab_size = dataset('english-german.pkl')

print(enc_seq_length, dec_seq_length, enc_vocab_size, dec_vocab_size)

# Prepare the training dataset batches
train_dataset = data.Dataset.from_tensor_slices((trainX, trainY))
train_dataset = train_dataset.batch(batch_size)

# Prepare the validation dataset batches
val_dataset = data.Dataset.from_tensor_slices((valX, valY))
val_dataset = val_dataset.batch(batch_size)

# Create model
training_model = TransformerModel(enc_vocab_size, dec_vocab_size, enc_seq_length, dec_seq_length, h, d_k, d_v, d_model, d_ff, n, dropout_rate)


# Defining the loss function
def loss_fcn(target, prediction):
    # Create mask so that the zero padding values are not included in the computation of loss
    padding_mask = math.logical_not(equal(target, 0))
    padding_mask = cast(padding_mask, float32)

    # Compute a sparse categorical cross-entropy loss on the unmasked values
    loss = sparse_categorical_crossentropy(target, prediction, from_logits=True) * padding_mask

    # Compute the mean loss over the unmasked values
    return reduce_sum(loss) / reduce_sum(padding_mask)


# Defining the accuracy function
def accuracy_fcn(target, prediction):
    # Create mask so that the zero padding values are not included in the computation of accuracy
    padding_mask = math.logical_not(equal(target, 0))

    # Find equal prediction and target values, and apply the padding mask
    accuracy = equal(target, argmax(prediction, axis=2))
    accuracy = math.logical_and(padding_mask, accuracy)

    # Cast the True/False values to 32-bit-precision floating-point numbers
    padding_mask = cast(padding_mask, float32)
    accuracy = cast(accuracy, float32)

    # Compute the mean accuracy over the unmasked values
    return reduce_sum(accuracy) / reduce_sum(padding_mask)


# Include metrics monitoring
train_loss = Mean(name='train_loss')
train_accuracy = Mean(name='train_accuracy')
val_loss = Mean(name='val_loss')

# Create a checkpoint object and manager to manage multiple checkpoints
ckpt = train.Checkpoint(model=training_model, optimizer=optimizer)
ckpt_manager = train.CheckpointManager(ckpt, "./checkpoints", max_to_keep=None)

# Initialise dictionaries to store the training and validation losses
train_loss_dict = {}
val_loss_dict = {}

# Speeding up the training process
@function
def train_step(encoder_input, decoder_input, decoder_output):
    with GradientTape() as tape:

        # Run the forward pass of the model to generate a prediction
        prediction = training_model(encoder_input, decoder_input, training=True)

        # Compute the training loss
        loss = loss_fcn(decoder_output, prediction)

        # Compute the training accuracy
        accuracy = accuracy_fcn(decoder_output, prediction)

    # Retrieve gradients of the trainable variables with respect to the training loss
    gradients = tape.gradient(loss, training_model.trainable_weights)

    # Update the values of the trainable variables by gradient descent
    optimizer.apply_gradients(zip(gradients, training_model.trainable_weights))

    train_loss(loss)
    train_accuracy(accuracy)


for epoch in range(epochs):

    train_loss.reset_states()
    train_accuracy.reset_states()
    val_loss.reset_states()

    print("\nStart of epoch %d" % (epoch + 1))

    start_time = time()

    # Iterate over the dataset batches
    for step, (train_batchX, train_batchY) in enumerate(train_dataset):

        # Define the encoder and decoder inputs, and the decoder output
        encoder_input = train_batchX[:, 1:]
        decoder_input = train_batchY[:, :-1]
        decoder_output = train_batchY[:, 1:]

        train_step(encoder_input, decoder_input, decoder_output)

        if step % 50 == 0:
            print(f'Epoch {epoch + 1} Step {step} Loss {train_loss.result():.4f} Accuracy {train_accuracy.result():.4f}')

    # Run a validation step after every epoch of training
    for val_batchX, val_batchY in val_dataset:

        # Define the encoder and decoder inputs, and the decoder output
        encoder_input = val_batchX[:, 1:]
        decoder_input = val_batchY[:, :-1]
        decoder_output = val_batchY[:, 1:]

        # Generate a prediction
        prediction = training_model(encoder_input, decoder_input, training=False)

        # Compute the validation loss
        loss = loss_fcn(decoder_output, prediction)
        val_loss(loss)

    # Print epoch number and accuracy and loss values at the end of every epoch
    print("Epoch %d: Training Loss %.4f, Training Accuracy %.4f, Validation Loss %.4f" % (epoch + 1, train_loss.result(), train_accuracy.result(), val_loss.result()))

    # Save a checkpoint after every epoch
    if (epoch + 1) % 1 == 0:

        save_path = ckpt_manager.save()
        print("Saved checkpoint at epoch %d" % (epoch + 1))

        # Save the trained model weights
        training_model.save_weights("weights/wghts" + str(epoch + 1) + ".ckpt")

        train_loss_dict[epoch] = train_loss.result()
        val_loss_dict[epoch] = val_loss.result()

# Save the training loss values
with open('./train_loss.pkl', 'wb') as file:
    dump(train_loss_dict, file)

# Save the validation loss values
with open('./val_loss.pkl', 'wb') as file:
    dump(val_loss_dict, file)

print("Total time taken: %.2fs" % (time() - start_time))

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from tensorflow.keras.optimizers import Adam

from tensorflow.keras.optimizers.schedules import LearningRateSchedule

from tensorflow.keras.metrics import Mean

from tensorflow import data, train, math, reduce_sum, cast, equal, argmax, float32, GradientTape, function

from keras.losses import sparse_categorical_crossentropy

from model import TransformerModel

from prepare_dataset import PrepareDataset

from time import time

from pickle import dump

# Define the model parameters

h = 8 # Number of self-attention heads

d_k = 64 # Dimensionality of the linearly projected queries and keys

d_v = 64 # Dimensionality of the linearly projected values

d_model = 512 # Dimensionality of model layers' outputs

d_ff = 2048 # Dimensionality of the inner fully connected layer

n = 6 # Number of layers in the encoder stack

# Define the training parameters

epochs = 20

batch_size = 64

beta_1 = 0.9

beta_2 = 0.98

epsilon = 1e-9

dropout_rate = 0.1

# Implementing a learning rate scheduler

class LRScheduler(LearningRateSchedule):

def __init__(self, d_model, warmup_steps=4000, **kwargs):

super(LRScheduler, self).__init__(**kwargs)

self.d_model = cast(d_model, float32)

self.warmup_steps = warmup_steps

def __call__(self, step_num):

# Linearly increasing the learning rate for the first warmup_steps, and decreasing it thereafter

arg1 = step_num ** -0.5

arg2 = step_num * (self.warmup_steps ** -1.5)

return (self.d_model ** -0.5) * math.minimum(arg1, arg2)

# Instantiate an Adam optimizer

optimizer = Adam(LRScheduler(d_model), beta_1, beta_2, epsilon)

# Prepare the training dataset

dataset = PrepareDataset()

trainX, trainY, valX, valY, train_orig, val_orig, enc_seq_length, dec_seq_length, enc_vocab_size, dec_vocab_size = dataset('english-german.pkl')

print(enc_seq_length, dec_seq_length, enc_vocab_size, dec_vocab_size)

# Prepare the training dataset batches

train_dataset = data.Dataset.from_tensor_slices((trainX, trainY))

train_dataset = train_dataset.batch(batch_size)

# Prepare the validation dataset batches

val_dataset = data.Dataset.from_tensor_slices((valX, valY))

val_dataset = val_dataset.batch(batch_size)

# Create model

training_model = TransformerModel(enc_vocab_size, dec_vocab_size, enc_seq_length, dec_seq_length, h, d_k, d_v, d_model, d_ff, n, dropout_rate)

# Defining the loss function

def loss_fcn(target, prediction):

# Create mask so that the zero padding values are not included in the computation of loss

padding_mask = math.logical_not(equal(target, 0))

padding_mask = cast(padding_mask, float32)

# Compute a sparse categorical cross-entropy loss on the unmasked values

loss = sparse_categorical_crossentropy(target, prediction, from_logits=True) * padding_mask

# Compute the mean loss over the unmasked values

return reduce_sum(loss) / reduce_sum(padding_mask)

# Defining the accuracy function

def accuracy_fcn(target, prediction):

# Create mask so that the zero padding values are not included in the computation of accuracy

padding_mask = math.logical_not(equal(target, 0))

# Find equal prediction and target values, and apply the padding mask

accuracy = equal(target, argmax(prediction, axis=2))

accuracy = math.logical_and(padding_mask, accuracy)

# Cast the True/False values to 32-bit-precision floating-point numbers

padding_mask = cast(padding_mask, float32)

accuracy = cast(accuracy, float32)

# Compute the mean accuracy over the unmasked values

return reduce_sum(accuracy) / reduce_sum(padding_mask)

# Include metrics monitoring

train_loss = Mean(name='train_loss')

train_accuracy = Mean(name='train_accuracy')

val_loss = Mean(name='val_loss')

# Create a checkpoint object and manager to manage multiple checkpoints

ckpt = train.Checkpoint(model=training_model, optimizer=optimizer)

ckpt_manager = train.CheckpointManager(ckpt, "./checkpoints", max_to_keep=None)

# Initialise dictionaries to store the training and validation losses

train_loss_dict = {}

val_loss_dict = {}

# Speeding up the training process

@function

def train_step(encoder_input, decoder_input, decoder_output):

with GradientTape() as tape:

# Run the forward pass of the model to generate a prediction

prediction = training_model(encoder_input, decoder_input, training=True)

# Compute the training loss

loss = loss_fcn(decoder_output, prediction)

# Compute the training accuracy

accuracy = accuracy_fcn(decoder_output, prediction)

# Retrieve gradients of the trainable variables with respect to the training loss

gradients = tape.gradient(loss, training_model.trainable_weights)

# Update the values of the trainable variables by gradient descent

optimizer.apply_gradients(zip(gradients, training_model.trainable_weights))

train_loss(loss)

train_accuracy(accuracy)

for epoch in range(epochs):

train_loss.reset_states()

train_accuracy.reset_states()

val_loss.reset_states()

print("\nStart of epoch %d" % (epoch + 1))

start_time = time()

# Iterate over the dataset batches

for step, (train_batchX, train_batchY) in enumerate(train_dataset):

# Define the encoder and decoder inputs, and the decoder output

encoder_input = train_batchX[:, 1:]

decoder_input = train_batchY[:, :-1]

decoder_output = train_batchY[:, 1:]

train_step(encoder_input, decoder_input, decoder_output)

if step % 50 == 0:

print(f'Epoch {epoch + 1} Step {step} Loss {train_loss.result():.4f} Accuracy {train_accuracy.result():.4f}')

# Run a validation step after every epoch of training

for val_batchX, val_batchY in val_dataset:

# Define the encoder and decoder inputs, and the decoder output

encoder_input = val_batchX[:, 1:]

decoder_input = val_batchY[:, :-1]

decoder_output = val_batchY[:, 1:]

# Generate a prediction

prediction = training_model(encoder_input, decoder_input, training=False)

# Compute the validation loss

loss = loss_fcn(decoder_output, prediction)

val_loss(loss)

# Print epoch number and accuracy and loss values at the end of every epoch

print("Epoch %d: Training Loss %.4f, Training Accuracy %.4f, Validation Loss %.4f" % (epoch + 1, train_loss.result(), train_accuracy.result(), val_loss.result()))

# Save a checkpoint after every epoch

if (epoch + 1) % 1 == 0:

save_path = ckpt_manager.save()

print("Saved checkpoint at epoch %d" % (epoch + 1))

# Save the trained model weights

training_model.save_weights("weights/wghts" + str(epoch + 1) + ".ckpt")

train_loss_dict[epoch] = train_loss.result()

val_loss_dict[epoch] = val_loss.result()

# Save the training loss values

with open('./train_loss.pkl', 'wb') as file:

dump(train_loss_dict, file)

# Save the validation loss values

with open('./val_loss.pkl', 'wb') as file:

dump(val_loss_dict, file)

print("Total time taken: %.2fs" % (time() - start_time))

Plotting the Training and Validation Loss Curves

In order to be able to plot the training and validation loss curves, you will first load the pickle files containing the training and validation loss dictionaries that you saved when training the Transformer model earlier.

Then you will retrieve the training and validation loss values from the respective dictionaries and graph them on the same plot.

The code listing is as follows, which you should save into a separate Python script:

from pickle import load
from matplotlib.pylab import plt
from numpy import arange

# Load the training and validation loss dictionaries
train_loss = load(open('train_loss.pkl', 'rb'))
val_loss = load(open('val_loss.pkl', 'rb'))

# Retrieve each dictionary's values
train_values = train_loss.values()
val_values = val_loss.values()

# Generate a sequence of integers to represent the epoch numbers
epochs = range(1, 21)

# Plot and label the training and validation loss values
plt.plot(epochs, train_values, label='Training Loss')
plt.plot(epochs, val_values, label='Validation Loss')

# Add in a title and axes labels
plt.title('Training and Validation Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')

# Set the tick locations
plt.xticks(arange(0, 21, 2))

# Display the plot
plt.legend(loc='best')
plt.show()

from pickle import load

from matplotlib.pylab import plt

from numpy import arange

# Load the training and validation loss dictionaries

train_loss = load(open('train_loss.pkl', 'rb'))

val_loss = load(open('val_loss.pkl', 'rb'))

# Retrieve each dictionary's values

train_values = train_loss.values()

val_values = val_loss.values()

# Generate a sequence of integers to represent the epoch numbers

epochs = range(1, 21)

# Plot and label the training and validation loss values

plt.plot(epochs, train_values, label='Training Loss')

plt.plot(epochs, val_values, label='Validation Loss')

# Add in a title and axes labels

plt.title('Training and Validation Loss')

plt.xlabel('Epochs')

plt.ylabel('Loss')

# Set the tick locations

plt.xticks(arange(0, 21, 2))

# Display the plot

plt.legend(loc='best')

plt.show()

Running the code above generates a similar plot of the training and validation loss curves to the one below:

Line plots of the training and validation loss values over several training epochs

Note that although you might see similar loss curves, they might not necessarily be identical to the ones above. This is because you are training the Transformer model from scratch, and the resulting training and validation loss values depend on the random initialization of the model weights.

Nonetheless, these loss curves give us a better insight into how the learning performance changes over the number of epochs and help us diagnose any problems with learning that can lead to an underfit or an overfit model.

For more details on using the training and validation loss curves to diagnose the learning performance of a model, you can refer to this tutorial by Jason Brownlee.

Summary

In this tutorial, you discovered how to plot the training and validation loss curves for the Transformer model.

Specifically, you learned:

How to modify the training code to include validation and test splits, in addition to a training split of the dataset
How to modify the training code to store the computed training and validation loss values, as well as the trained model weights
How to plot the saved training and validation loss curves

Do you have any questions?
Ask your questions in the comments below, and I will do my best to answer.

5 Responses to Plotting the Training and Validation Loss Curves for the Transformer Model

Brett November 3, 2022 at 7:11 am #

To get this to work, I had to cast the two items to a list (lines 6 & 7), like this:

# Retrieve each dictionary’s values
train_values = list(train_loss.values())
val_values = list(val_loss.values())

Great series, thanks!

khatija March 9, 2023 at 3:11 am #

How can plot accuracy for each individual category in classifiction of image after trainnig

- James Carmichael March 9, 2023 at 9:35 am #
  
  Hi khatija…The following resource may be of interest:
  
  https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/
  
Olufunke May 6, 2023 at 12:01 am #

Hi, thanks so much. I am trying to adapt this for pytorch. Please how do I define the weights

- James Carmichael May 6, 2023 at 10:25 am #
  
  Hi Olufunke…The following discussions may be of interest:
  
  https://stackoverflow.com/questions/74754493/plot-training-and-validation-loss-in-pytorch
  
  https://medium.datadriveninvestor.com/visualizing-training-and-validation-loss-in-real-time-using-pytorch-and-bokeh-5522401bc9dd

Navigation

Plotting the Training and Validation Loss Curves for the Transformer Model

Tutorial Overview

Prerequisites

Recap of the Transformer Architecture

Want to Get Started With Building Transformer Models with Attention?

Preparing the Training, Validation, and Testing Splits of the Dataset

Training the Transformer Model

Plotting the Training and Validation Loss Curves

Further Reading

Books

Papers

Websites

Summary

Learn Transformers and Attention!

Teach your deep learning model to read a sentence

Give magical power of understanding human language for
Your Projects

More On This Topic

5 Responses to Plotting the Training and Validation Loss Curves for the Transformer Model

Leave a Reply Click here to cancel reply.

Navigation

Tutorial Overview

Prerequisites

Recap of the Transformer Architecture

Want to Get Started With Building Transformer Models with Attention?

Preparing the Training, Validation, and Testing Splits of the Dataset

Training the Transformer Model

Plotting the Training and Validation Loss Curves

Further Reading

Books

Papers

Websites

Summary

Learn Transformers and Attention!

Teach your deep learning model to read a sentence

Give magical power of understanding human language for Your Projects

More On This Topic

5 Responses to Plotting the Training and Validation Loss Curves for the Transformer Model

Leave a Reply Click here to cancel reply.

Give magical power of understanding human language for
Your Projects