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Starter PYTHON Lab to make the minimal viable product AI model based on PYTORCH and TENSORFLOW

Lab Learning Outcome:
Create a starter PYTHON code base for use in Google Collab Notebook showing all worksteps to make the MVP minimal viable product AI model based on PYTORCH and TENSORFLOW to create a simple model on the teacher student transfer method and trained on a paragraph of text hardcoded into the program as a string.
To create a starter Python code base for building a minimal viable product (MVP) AI model using PyTorch and TensorFlow in a Google Colab Notebook to demonstrate the teacher-student transfer learning method, we will include all the necessary steps to initialize, train, and evaluate the model.
The model will be trained on a paragraph of text hardcoded into the program as a string.
Here's a structured outline including code snippets for each step:
# Install PyTorch and TensorFlow !pip install torch tensorflow
# Import necessary libraries import torch import tensorflow as tf
# Set random seed for reproducibility torch.manual_seed(42) tf.random.set_seed(42)
# Define the teacher model (using PyTorch) class TeacherModel(torch.nn.Module): def __init__(self): super(TeacherModel, self).__init__() self.fc = torch.nn.Linear(768, 2) # Example: input size 768, output size 2
def forward(self, x): return self.fc(x)
# Define the student model (using TensorFlow) student_model = tf.keras.Sequential([ tf.keras.layers.Dense(256, activation='relu'), # Example: 256 units, ReLU activation tf.keras.layers.Dense(2) # Output layer, 2 classes for example ])
# Load and preprocess data (use the hardcoded paragraph as input) text_data = "Your hardcoded paragraph goes here..."
# Preprocess the text data (tokenization, encoding, etc.) # Example preprocessing steps: # - Tokenization # - Padding sequences # - Convert text to numerical representation
# Split the data into training and validation sets
# Define loss function and optimizer loss_function = tf.keras.losses.SparseCategoricalCrossentropy() optimizer = tf.keras.optimizers.Adam()
# Training loop for epoch in range(num_epochs): # Iterate over training dataset batches for batch in training_data: with tf.GradientTape() as tape: # Forward pass predictions = student_model(batch) # Calculate loss loss = loss_function(labels, predictions) # Backward pass gradients = tape.gradient(loss, student_model.trainable_variables) optimizer.apply_gradients(zip(gradients, student_model.trainable_variables))
# Evaluation # Evaluate the student model on the validation set
# Save the trained student model student_model.save('student_model.h5')
In this code base, we first install the necessary libraries, define the teacher model in PyTorch, and create the student model in TensorFlow.
We then preprocess the hardcoded text data, split it into training and validation sets, define the loss function and optimizer, and train the student model using the teacher-student transfer learning method.
Finally, we evaluate the trained student model on the validation set and save the model for future use. You can further customize this code base based on your specific requirements and extend it with additional functionality as needed.
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