A neural network library built on top of TensorFlow for quickly building deep learning models.
Project description
A neural network library built on top of TensorFlow for quickly building deep learning models.
Installation
pip install nn
Example
import nn
# Create the model
@nn.model
def model(inputs):
# Define the network architecture (layers, number of units, activations)
hidden = nn.Dense(units=64, activation='relu')(inputs)
outputs = nn.Dense(units=10)(hidden)
# Configure the learning process (loss, optimizer, evaluation metrics)
return dict(outputs=outputs,
loss='softmax_cross_entropy',
optimizer=('GradientDescent', 0.001),
metrics=['accuracy'])
# Train the model using training data:
model.train(x_train, y_train, epochs=30, batch_size=128)
# Evaluate the model performance on test or validation data:
loss_and_metrics = model.evaluate(x_test, y_test)
# Use the model to make predictions for new data:
predictions = model.predict(x)
Documentation
See documentation.
License
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