Created
April 25, 2022 16:17
-
-
Save SherazKhan/90c327c1826ff1b391f9352fd1081f1e to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import numpy as np | |
import tensorflow.keras as keras | |
def build_model(input_shape): | |
"""Generates RNN-LSTM model | |
:param input_shape (tuple): Shape of input set | |
:return model: RNN-LSTM model | |
""" | |
# build network topology | |
model = keras.Sequential() | |
# 3 Bidirectional LSTM layers | |
model.add(keras.layers.Bidirectional(keras.layers.LSTM(216, return_sequences=True, activation="tanh", dropout=0.5), | |
input_shape=input_shape)) | |
model.add(keras.layers.Bidirectional(keras.layers.LSTM(216, return_sequences=True, activation="tanh", dropout=0.4))) | |
model.add(keras.layers.Bidirectional(keras.layers.LSTM(216, activation="tanh", dropout=0.3))) | |
# dense layer | |
model.add(keras.layers.Dense(64, activation='relu')) | |
model.add(keras.layers.Dropout(0.3)) | |
# output layer | |
model.add(keras.layers.Dense(1, activation='sigmoid')) | |
optimiser = keras.optimizers.Adam(learning_rate=0.0001) | |
model.compile(optimizer=optimiser, | |
loss='binary_crossentropy', | |
metrics=['accuracy']) | |
model.summary() | |
return model | |
# input_shape = (X_train.shape[1], X_train.shape[2]) # 130, 13 | |
# model = build_model(input_shape) | |
# history = model.fit(X_train, y_train, validation_data=(X_validation, y_validation), batch_size=32, epochs=30) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment