Keras
A high level API that allows for quick development of neural networks.
Keras API Reference
Example Model
from keras.models import Sequential
from keras.layers import Input, Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import LSTM
hidden_size = 64
# Initialize the model
model = Sequential()
# Specify the input to the model
model.add(Input(shape=(X_train.shape[1], 1)))
# Add a Long Short-Term Memory Layer (form of RNN) with 64 neurons
model.add(LSTM(hidden_size))
# Add a Dropout Layer, which randomly sets inputs to 0 35% of the time
# This is a regularization technique commonly used to prevent overfitting
model.add(Dropout(0.35))
# Add a Dense Layer, which is a layer of neurons in a neural network that
# receive inputs from all neurons from the previous layer.
# In this case, this is our output layer.
model.add(Dense(1))
# Adds an Activation Layer, which simply provides an activation function
model.add(Activation('sigmoid'))
# Compile the model, specifying the loss function and optimizer to use
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['mae', 'acc'])
print(model.summary())