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Chapter 7: Understanding Autoregressive Models
7.4 Practical Exercises - Chapter 7: Understanding Autoregressive Models
This section provides practical exercises to reinforce your understanding of autoregressive models and their applications. Each exercise includes a problem statement and a solution with code examples where applicable.
Exercise 1: Implement a Simple Text Generation Model
Problem Statement: Implement a simple text generation model using the GPT-2 architecture. Fine-tune the model on a small custom dataset and generate text based on a given prompt.
Solution:
from transformers import GPT2Tokenizer, TFGPT2LMHeadModel, TextDataset, DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments
# Load pre-trained GPT-2 tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = TFGPT2LMHeadModel.from_pretrained("gpt2")
# Prepare a small custom dataset
text = "Your custom dataset text goes here. Make sure to have a substantial amount of text for fine-tuning."
with open("custom_dataset.txt", "w") as f:
f.write(text)
# Load the dataset and prepare for training
dataset = TextDataset(
tokenizer=tokenizer,
file_path="custom_dataset.txt",
block_size=128
)
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=False
)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
overwrite_output_dir=True,
num_train_epochs=3,
per_device_train_batch_size=2,
save_steps=10_000,
save_total_limit=2,
)
# Fine-tune the model
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset,
)
trainer.train()
# Generate text using the fine-tuned model
input_text = "Once upon a time"
input_ids = tokenizer.encode(input_text, return_tensors='tf')
# Generate text
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)Exercise 2: Translate Text Using a Transformer Model
Problem Statement: Use a pre-trained MarianMT model to translate a given English sentence to German.
Solution:
from transformers import MarianMTModel, MarianTokenizer
# Load pre-trained MarianMT model and tokenizer
model_name = 'Helsinki-NLP/opus-mt-en-de'
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
# Define the input text
text = "Hello, how are you?"
# Tokenize the input text
inputs = tokenizer(text, return_tensors="pt")
# Perform translation
translated = model.generate(**inputs)
# Decode the translated text
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
print(translated_text)Exercise 3: Summarize a Long Text
Problem Statement: Implement a text summarization model using GPT-3. Provide a long text and generate a concise summary.
Solution:
import openai
# Set up OpenAI API key
openai.api_key = 'your-api-key-here'
# Define the prompt for summarization
prompt = ("Summarize the following text:\\n\\n"
"Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. "
"Leading AI textbooks define the field as the study of 'intelligent agents': any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. "
"Colloquially, the term 'artificial intelligence' is often used to describe machines (or computers) that mimic 'cognitive' functions that humans associate with the human mind, "
"such as 'learning' and 'problem solving'.")
# Generate summary using GPT-3
response = openai.Completion.create(
engine="davinci",
prompt=prompt,
max_tokens=60,
n=1,
stop=None,
temperature=0.7
)
# Print the generated summary
print(response.choices[0].text.strip())Exercise 4: Generate an Image Using PixelCNN
Problem Statement: Implement a simple PixelCNN model to generate an image based on random noise input.
Solution:
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Input, Conv2D
from tensorflow.keras.models import Model
# Define the PixelCNN model (simplified version)
def build_pixelcnn(input_shape):
inputs = Input(shape=input_shape)
x = Conv2D(64, (7, 7), padding='same', activation='relu')(inputs)
x = Conv2D(64, (7, 7), padding='same', activation='relu')(x)
outputs = Conv2D(1, (1, 1), activation='sigmoid')(x)
return Model(inputs, outputs, name='pixelcnn')
# Generate random noise as input
input_shape = (28, 28, 1)
noise = np.random.rand(1, *input_shape)
# Build the PixelCNN model
pixelcnn = build_pixelcnn(input_shape)
pixelcnn.compile(optimizer='adam', loss='binary_crossentropy')
# Generate an image (for demonstration purposes, normally you would train the model first)
generated_image = pixelcnn.predict(noise).reshape(28, 28)
# Display the generated image
plt.imshow(generated_image, cmap='gray')
plt.axis('off')
plt.show()Exercise 5: Generate Speech with WaveNet (Conceptual)
Problem Statement: Implement a conceptual WaveNet model for speech generation.
Solution:
# Note: This is a conceptual example. Implementing WaveNet from scratch requires significant computational resources.
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv1D, Add, Activation
from tensorflow.keras.models import Model
# Define the WaveNet model (simplified version)
def build_wavenet(input_shape):
inputs = Input(shape=input_shape)
x = Conv1D(64, kernel_size=2, dilation_rate=1, padding='causal', activation='relu')(inputs)
for dilation_rate in [2, 4, 8, 16]:
x = Conv1D(64, kernel_size=2, dilation_rate=dilation_rate, padding='causal', activation='relu')(x)
x = Add()([inputs, x])
outputs = Conv1D(1, kernel_size=1, activation='tanh')(x)
return Model(inputs, outputs, name='wavenet')
# Build the WaveNet model
input_shape = (None, 1) # Variable length input
wavenet = build_wavenet(input_shape)
wavenet.summary()
# Generate a waveform (for demonstration purposes, normally you would train the model first)
input_waveform = np.random.rand(1, 16000, 1) # 1-second random noise at 16kHz
generated_waveform = wavenet.predict(input_waveform).reshape(-1)
# Display the generated waveform
plt.plot(generated_waveform[:1000]) # Display the first 1000 samples
plt.show()These practical exercises provide hands-on experience with autoregressive models and their applications. By implementing text generation, translation, summarization, image generation, and speech generation models, you can deepen your understanding of how these powerful models work and how they can be applied to real-world tasks. Each exercise is designed to reinforce key concepts and techniques, helping you to leverage autoregressive models effectively in your projects.
7.4 Practical Exercises - Chapter 7: Understanding Autoregressive Models
This section provides practical exercises to reinforce your understanding of autoregressive models and their applications. Each exercise includes a problem statement and a solution with code examples where applicable.
Exercise 1: Implement a Simple Text Generation Model
Problem Statement: Implement a simple text generation model using the GPT-2 architecture. Fine-tune the model on a small custom dataset and generate text based on a given prompt.
Solution:
from transformers import GPT2Tokenizer, TFGPT2LMHeadModel, TextDataset, DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments
# Load pre-trained GPT-2 tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = TFGPT2LMHeadModel.from_pretrained("gpt2")
# Prepare a small custom dataset
text = "Your custom dataset text goes here. Make sure to have a substantial amount of text for fine-tuning."
with open("custom_dataset.txt", "w") as f:
f.write(text)
# Load the dataset and prepare for training
dataset = TextDataset(
tokenizer=tokenizer,
file_path="custom_dataset.txt",
block_size=128
)
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=False
)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
overwrite_output_dir=True,
num_train_epochs=3,
per_device_train_batch_size=2,
save_steps=10_000,
save_total_limit=2,
)
# Fine-tune the model
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset,
)
trainer.train()
# Generate text using the fine-tuned model
input_text = "Once upon a time"
input_ids = tokenizer.encode(input_text, return_tensors='tf')
# Generate text
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)Exercise 2: Translate Text Using a Transformer Model
Problem Statement: Use a pre-trained MarianMT model to translate a given English sentence to German.
Solution:
from transformers import MarianMTModel, MarianTokenizer
# Load pre-trained MarianMT model and tokenizer
model_name = 'Helsinki-NLP/opus-mt-en-de'
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
# Define the input text
text = "Hello, how are you?"
# Tokenize the input text
inputs = tokenizer(text, return_tensors="pt")
# Perform translation
translated = model.generate(**inputs)
# Decode the translated text
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
print(translated_text)Exercise 3: Summarize a Long Text
Problem Statement: Implement a text summarization model using GPT-3. Provide a long text and generate a concise summary.
Solution:
import openai
# Set up OpenAI API key
openai.api_key = 'your-api-key-here'
# Define the prompt for summarization
prompt = ("Summarize the following text:\\n\\n"
"Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. "
"Leading AI textbooks define the field as the study of 'intelligent agents': any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. "
"Colloquially, the term 'artificial intelligence' is often used to describe machines (or computers) that mimic 'cognitive' functions that humans associate with the human mind, "
"such as 'learning' and 'problem solving'.")
# Generate summary using GPT-3
response = openai.Completion.create(
engine="davinci",
prompt=prompt,
max_tokens=60,
n=1,
stop=None,
temperature=0.7
)
# Print the generated summary
print(response.choices[0].text.strip())Exercise 4: Generate an Image Using PixelCNN
Problem Statement: Implement a simple PixelCNN model to generate an image based on random noise input.
Solution:
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Input, Conv2D
from tensorflow.keras.models import Model
# Define the PixelCNN model (simplified version)
def build_pixelcnn(input_shape):
inputs = Input(shape=input_shape)
x = Conv2D(64, (7, 7), padding='same', activation='relu')(inputs)
x = Conv2D(64, (7, 7), padding='same', activation='relu')(x)
outputs = Conv2D(1, (1, 1), activation='sigmoid')(x)
return Model(inputs, outputs, name='pixelcnn')
# Generate random noise as input
input_shape = (28, 28, 1)
noise = np.random.rand(1, *input_shape)
# Build the PixelCNN model
pixelcnn = build_pixelcnn(input_shape)
pixelcnn.compile(optimizer='adam', loss='binary_crossentropy')
# Generate an image (for demonstration purposes, normally you would train the model first)
generated_image = pixelcnn.predict(noise).reshape(28, 28)
# Display the generated image
plt.imshow(generated_image, cmap='gray')
plt.axis('off')
plt.show()Exercise 5: Generate Speech with WaveNet (Conceptual)
Problem Statement: Implement a conceptual WaveNet model for speech generation.
Solution:
# Note: This is a conceptual example. Implementing WaveNet from scratch requires significant computational resources.
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv1D, Add, Activation
from tensorflow.keras.models import Model
# Define the WaveNet model (simplified version)
def build_wavenet(input_shape):
inputs = Input(shape=input_shape)
x = Conv1D(64, kernel_size=2, dilation_rate=1, padding='causal', activation='relu')(inputs)
for dilation_rate in [2, 4, 8, 16]:
x = Conv1D(64, kernel_size=2, dilation_rate=dilation_rate, padding='causal', activation='relu')(x)
x = Add()([inputs, x])
outputs = Conv1D(1, kernel_size=1, activation='tanh')(x)
return Model(inputs, outputs, name='wavenet')
# Build the WaveNet model
input_shape = (None, 1) # Variable length input
wavenet = build_wavenet(input_shape)
wavenet.summary()
# Generate a waveform (for demonstration purposes, normally you would train the model first)
input_waveform = np.random.rand(1, 16000, 1) # 1-second random noise at 16kHz
generated_waveform = wavenet.predict(input_waveform).reshape(-1)
# Display the generated waveform
plt.plot(generated_waveform[:1000]) # Display the first 1000 samples
plt.show()These practical exercises provide hands-on experience with autoregressive models and their applications. By implementing text generation, translation, summarization, image generation, and speech generation models, you can deepen your understanding of how these powerful models work and how they can be applied to real-world tasks. Each exercise is designed to reinforce key concepts and techniques, helping you to leverage autoregressive models effectively in your projects.
7.4 Practical Exercises - Chapter 7: Understanding Autoregressive Models
This section provides practical exercises to reinforce your understanding of autoregressive models and their applications. Each exercise includes a problem statement and a solution with code examples where applicable.
Exercise 1: Implement a Simple Text Generation Model
Problem Statement: Implement a simple text generation model using the GPT-2 architecture. Fine-tune the model on a small custom dataset and generate text based on a given prompt.
Solution:
from transformers import GPT2Tokenizer, TFGPT2LMHeadModel, TextDataset, DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments
# Load pre-trained GPT-2 tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = TFGPT2LMHeadModel.from_pretrained("gpt2")
# Prepare a small custom dataset
text = "Your custom dataset text goes here. Make sure to have a substantial amount of text for fine-tuning."
with open("custom_dataset.txt", "w") as f:
f.write(text)
# Load the dataset and prepare for training
dataset = TextDataset(
tokenizer=tokenizer,
file_path="custom_dataset.txt",
block_size=128
)
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=False
)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
overwrite_output_dir=True,
num_train_epochs=3,
per_device_train_batch_size=2,
save_steps=10_000,
save_total_limit=2,
)
# Fine-tune the model
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset,
)
trainer.train()
# Generate text using the fine-tuned model
input_text = "Once upon a time"
input_ids = tokenizer.encode(input_text, return_tensors='tf')
# Generate text
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)Exercise 2: Translate Text Using a Transformer Model
Problem Statement: Use a pre-trained MarianMT model to translate a given English sentence to German.
Solution:
from transformers import MarianMTModel, MarianTokenizer
# Load pre-trained MarianMT model and tokenizer
model_name = 'Helsinki-NLP/opus-mt-en-de'
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
# Define the input text
text = "Hello, how are you?"
# Tokenize the input text
inputs = tokenizer(text, return_tensors="pt")
# Perform translation
translated = model.generate(**inputs)
# Decode the translated text
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
print(translated_text)Exercise 3: Summarize a Long Text
Problem Statement: Implement a text summarization model using GPT-3. Provide a long text and generate a concise summary.
Solution:
import openai
# Set up OpenAI API key
openai.api_key = 'your-api-key-here'
# Define the prompt for summarization
prompt = ("Summarize the following text:\\n\\n"
"Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. "
"Leading AI textbooks define the field as the study of 'intelligent agents': any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. "
"Colloquially, the term 'artificial intelligence' is often used to describe machines (or computers) that mimic 'cognitive' functions that humans associate with the human mind, "
"such as 'learning' and 'problem solving'.")
# Generate summary using GPT-3
response = openai.Completion.create(
engine="davinci",
prompt=prompt,
max_tokens=60,
n=1,
stop=None,
temperature=0.7
)
# Print the generated summary
print(response.choices[0].text.strip())Exercise 4: Generate an Image Using PixelCNN
Problem Statement: Implement a simple PixelCNN model to generate an image based on random noise input.
Solution:
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Input, Conv2D
from tensorflow.keras.models import Model
# Define the PixelCNN model (simplified version)
def build_pixelcnn(input_shape):
inputs = Input(shape=input_shape)
x = Conv2D(64, (7, 7), padding='same', activation='relu')(inputs)
x = Conv2D(64, (7, 7), padding='same', activation='relu')(x)
outputs = Conv2D(1, (1, 1), activation='sigmoid')(x)
return Model(inputs, outputs, name='pixelcnn')
# Generate random noise as input
input_shape = (28, 28, 1)
noise = np.random.rand(1, *input_shape)
# Build the PixelCNN model
pixelcnn = build_pixelcnn(input_shape)
pixelcnn.compile(optimizer='adam', loss='binary_crossentropy')
# Generate an image (for demonstration purposes, normally you would train the model first)
generated_image = pixelcnn.predict(noise).reshape(28, 28)
# Display the generated image
plt.imshow(generated_image, cmap='gray')
plt.axis('off')
plt.show()Exercise 5: Generate Speech with WaveNet (Conceptual)
Problem Statement: Implement a conceptual WaveNet model for speech generation.
Solution:
# Note: This is a conceptual example. Implementing WaveNet from scratch requires significant computational resources.
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv1D, Add, Activation
from tensorflow.keras.models import Model
# Define the WaveNet model (simplified version)
def build_wavenet(input_shape):
inputs = Input(shape=input_shape)
x = Conv1D(64, kernel_size=2, dilation_rate=1, padding='causal', activation='relu')(inputs)
for dilation_rate in [2, 4, 8, 16]:
x = Conv1D(64, kernel_size=2, dilation_rate=dilation_rate, padding='causal', activation='relu')(x)
x = Add()([inputs, x])
outputs = Conv1D(1, kernel_size=1, activation='tanh')(x)
return Model(inputs, outputs, name='wavenet')
# Build the WaveNet model
input_shape = (None, 1) # Variable length input
wavenet = build_wavenet(input_shape)
wavenet.summary()
# Generate a waveform (for demonstration purposes, normally you would train the model first)
input_waveform = np.random.rand(1, 16000, 1) # 1-second random noise at 16kHz
generated_waveform = wavenet.predict(input_waveform).reshape(-1)
# Display the generated waveform
plt.plot(generated_waveform[:1000]) # Display the first 1000 samples
plt.show()These practical exercises provide hands-on experience with autoregressive models and their applications. By implementing text generation, translation, summarization, image generation, and speech generation models, you can deepen your understanding of how these powerful models work and how they can be applied to real-world tasks. Each exercise is designed to reinforce key concepts and techniques, helping you to leverage autoregressive models effectively in your projects.
7.4 Practical Exercises - Chapter 7: Understanding Autoregressive Models
This section provides practical exercises to reinforce your understanding of autoregressive models and their applications. Each exercise includes a problem statement and a solution with code examples where applicable.
Exercise 1: Implement a Simple Text Generation Model
Problem Statement: Implement a simple text generation model using the GPT-2 architecture. Fine-tune the model on a small custom dataset and generate text based on a given prompt.
Solution:
from transformers import GPT2Tokenizer, TFGPT2LMHeadModel, TextDataset, DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments
# Load pre-trained GPT-2 tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = TFGPT2LMHeadModel.from_pretrained("gpt2")
# Prepare a small custom dataset
text = "Your custom dataset text goes here. Make sure to have a substantial amount of text for fine-tuning."
with open("custom_dataset.txt", "w") as f:
f.write(text)
# Load the dataset and prepare for training
dataset = TextDataset(
tokenizer=tokenizer,
file_path="custom_dataset.txt",
block_size=128
)
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=False
)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
overwrite_output_dir=True,
num_train_epochs=3,
per_device_train_batch_size=2,
save_steps=10_000,
save_total_limit=2,
)
# Fine-tune the model
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset,
)
trainer.train()
# Generate text using the fine-tuned model
input_text = "Once upon a time"
input_ids = tokenizer.encode(input_text, return_tensors='tf')
# Generate text
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)Exercise 2: Translate Text Using a Transformer Model
Problem Statement: Use a pre-trained MarianMT model to translate a given English sentence to German.
Solution:
from transformers import MarianMTModel, MarianTokenizer
# Load pre-trained MarianMT model and tokenizer
model_name = 'Helsinki-NLP/opus-mt-en-de'
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
# Define the input text
text = "Hello, how are you?"
# Tokenize the input text
inputs = tokenizer(text, return_tensors="pt")
# Perform translation
translated = model.generate(**inputs)
# Decode the translated text
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
print(translated_text)Exercise 3: Summarize a Long Text
Problem Statement: Implement a text summarization model using GPT-3. Provide a long text and generate a concise summary.
Solution:
import openai
# Set up OpenAI API key
openai.api_key = 'your-api-key-here'
# Define the prompt for summarization
prompt = ("Summarize the following text:\\n\\n"
"Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. "
"Leading AI textbooks define the field as the study of 'intelligent agents': any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. "
"Colloquially, the term 'artificial intelligence' is often used to describe machines (or computers) that mimic 'cognitive' functions that humans associate with the human mind, "
"such as 'learning' and 'problem solving'.")
# Generate summary using GPT-3
response = openai.Completion.create(
engine="davinci",
prompt=prompt,
max_tokens=60,
n=1,
stop=None,
temperature=0.7
)
# Print the generated summary
print(response.choices[0].text.strip())Exercise 4: Generate an Image Using PixelCNN
Problem Statement: Implement a simple PixelCNN model to generate an image based on random noise input.
Solution:
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Input, Conv2D
from tensorflow.keras.models import Model
# Define the PixelCNN model (simplified version)
def build_pixelcnn(input_shape):
inputs = Input(shape=input_shape)
x = Conv2D(64, (7, 7), padding='same', activation='relu')(inputs)
x = Conv2D(64, (7, 7), padding='same', activation='relu')(x)
outputs = Conv2D(1, (1, 1), activation='sigmoid')(x)
return Model(inputs, outputs, name='pixelcnn')
# Generate random noise as input
input_shape = (28, 28, 1)
noise = np.random.rand(1, *input_shape)
# Build the PixelCNN model
pixelcnn = build_pixelcnn(input_shape)
pixelcnn.compile(optimizer='adam', loss='binary_crossentropy')
# Generate an image (for demonstration purposes, normally you would train the model first)
generated_image = pixelcnn.predict(noise).reshape(28, 28)
# Display the generated image
plt.imshow(generated_image, cmap='gray')
plt.axis('off')
plt.show()Exercise 5: Generate Speech with WaveNet (Conceptual)
Problem Statement: Implement a conceptual WaveNet model for speech generation.
Solution:
# Note: This is a conceptual example. Implementing WaveNet from scratch requires significant computational resources.
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv1D, Add, Activation
from tensorflow.keras.models import Model
# Define the WaveNet model (simplified version)
def build_wavenet(input_shape):
inputs = Input(shape=input_shape)
x = Conv1D(64, kernel_size=2, dilation_rate=1, padding='causal', activation='relu')(inputs)
for dilation_rate in [2, 4, 8, 16]:
x = Conv1D(64, kernel_size=2, dilation_rate=dilation_rate, padding='causal', activation='relu')(x)
x = Add()([inputs, x])
outputs = Conv1D(1, kernel_size=1, activation='tanh')(x)
return Model(inputs, outputs, name='wavenet')
# Build the WaveNet model
input_shape = (None, 1) # Variable length input
wavenet = build_wavenet(input_shape)
wavenet.summary()
# Generate a waveform (for demonstration purposes, normally you would train the model first)
input_waveform = np.random.rand(1, 16000, 1) # 1-second random noise at 16kHz
generated_waveform = wavenet.predict(input_waveform).reshape(-1)
# Display the generated waveform
plt.plot(generated_waveform[:1000]) # Display the first 1000 samples
plt.show()These practical exercises provide hands-on experience with autoregressive models and their applications. By implementing text generation, translation, summarization, image generation, and speech generation models, you can deepen your understanding of how these powerful models work and how they can be applied to real-world tasks. Each exercise is designed to reinforce key concepts and techniques, helping you to leverage autoregressive models effectively in your projects.