Examples

PyDeepFlow Examples

Here are some practical examples demonstrating how to use PyDeepFlow for different types of neural network models.

1. Binary Classification:

# Import necessary modules
import numpy as np
from pydeepflow.model import Multi_Layer_ANN

# Sample data
X_train = np.random.randn(500, 10)
y_train = np.random.randint(0, 2, size=(500, 1))

# Define and compile the model
model = Multi_Layer_ANN(X_train, y_train, hidden_layers=[64], activations=['relu'], loss='binary_crossentropy')

# Train the model
model.fit(epochs=100, learning_rate=0.01)

2. Multiclass Classification:

# Sample data
X_train = np.random.randn(1000, 20)
y_train = np.eye(3)[np.random.randint(0, 3, 1000)]  # One-hot encoded labels for 3 classes

# Define and compile the model
model = Multi_Layer_ANN(X_train, y_train, hidden_layers=[64, 32], activations=['relu', 'relu'], loss='categorical_crossentropy')

# Train the model
model.fit(epochs=50, learning_rate=0.001)

For more advanced examples, refer to the Advanced Features section.

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