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Model definition, compiling, fitting, evaluating

Description

Model development in deep learning follows a pipeline involving:

  • Model Definition: Creating the architecture of the model using layers and configuration.
  • Compiling: Configuring the model with optimizer, loss function, and metrics.
  • Fitting: Training the model on data by adjusting weights based on the loss function.
  • Evaluating: Measuring the model’s performance on test or validation data.

This process is streamlined using high-level libraries like TensorFlow (Keras API) or PyTorch. While TensorFlow/Keras is more declarative, PyTorch allows greater flexibility with dynamic computation graphs.

Did You Know?

Model compilation does not occur in PyTorch the same way it does in Keras. In PyTorch, optimizers and loss functions are set manually before training.

Examples

Here's a simple model pipeline example using both TensorFlow/Keras and PyTorch:

TensorFlow/Keras

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam

# Define model
model = Sequential([
    Dense(64, activation='relu', input_shape=(100,)),
    Dense(10, activation='softmax')
])

# Compile model
model.compile(optimizer=Adam(learning_rate=0.001),
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Fit model
model.fit(X_train, y_train, epochs=10, batch_size=32)

# Evaluate model
loss, accuracy = model.evaluate(X_test, y_test)

PyTorch

import torch
import torch.nn as nn
import torch.optim as optim

class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc1 = nn.Linear(100, 64)
        self.fc2 = nn.Linear(64, 10)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return torch.softmax(self.fc2(x), dim=1)

model = SimpleNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop
for epoch in range(10):
    optimizer.zero_grad()
    outputs = model(X_train)
    loss = criterion(outputs, y_train)
    loss.backward()
    optimizer.step()

# Evaluation (basic)
with torch.no_grad():
    predictions = model(X_test)

Real-World Applications

Medical Imaging Diagnosis

Compiling and training models using Keras or PyTorch for automated disease detection from X-rays and MRIs.

Self-Driving Cars

Real-time neural network models trained and evaluated using PyTorch to interpret camera and sensor data.

Search Engines

Search ranking models developed and fine-tuned using TensorFlow to optimize result relevance.

Video Recommendations

Neural networks trained using TensorFlow/Keras on viewing history data to suggest personalized videos.

Resources

Video Tutorials

below is the video resource

PDFs

The following documents

Recommended Books

Interview Questions

What is the purpose of compiling a model in Keras?

Compiling a model in Keras configures the training process. It sets the optimizer, loss function, and evaluation metrics that the model will use when trained and evaluated.

How does model training differ in TensorFlow and PyTorch?

TensorFlow typically uses a high-level API (Keras) where training is done via fit(). PyTorch uses a manual training loop offering greater flexibility and control over each step (forward, loss, backward, step).

What is the role of the optimizer during training?

The optimizer updates the weights of the model to minimize the loss function. It uses the gradients calculated during backpropagation to perform this update.

What are common evaluation metrics for classification tasks?

  • Accuracy
  • Precision, Recall, F1-score
  • ROC-AUC
  • Confusion Matrix