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models.py
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models.py
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# Copyright (C) 2019 Emmanuel LC. de los Santos
# University of Warwick
# Warwick Integrative Synthetic Biology Centre
#
# License: GNU Affero General Public License v3 or later
# A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt.
'''
This file is part of NeuRiPP.
NeuRiPP is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
NeuRiPP is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with NeuRiPP. If not, see <http://www.gnu.org/licenses/>.
'''
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
def create_model_conv_parallel():
input = keras.layers.Input(shape=(120, 20))
kernel_sizes = [3,4,5]
maps_per_kernel = 2
convs = []
for kernel_size in kernel_sizes:
for map_n in range(maps_per_kernel):
conv = keras.layers.Conv1D(filters=50,
kernel_size=kernel_size,
padding='valid',
activation='relu',
kernel_initializer='glorot_normal',
strides=1)(input)
conv_drop = keras.layers.Dropout(0.1)(conv)
max_pool = keras.layers.MaxPooling1D(3)(conv_drop)
convs.append(max_pool)
merge = keras.layers.Concatenate(axis=1)(convs)
mix = keras.layers.Conv1D(filters=150,
kernel_size=3,
padding='valid',
activation='relu',
kernel_initializer='glorot_normal',
strides=1)(merge)
max = keras.layers.MaxPooling1D(3)(mix)
flatten = keras.layers.Flatten()(max)
dense = keras.layers.Dense(60, activation='relu')(flatten)
drop = keras.layers.Dropout(0.5)(dense)
output = keras.layers.Dense(2, activation='sigmoid')(drop)
model = tf.keras.Model(inputs=input, outputs=output)
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def create_model_conv_parallel_lstm():
input = keras.layers.Input(shape=(120, 20))
kernel_sizes = [3,4,5]
maps_per_kernel = 2
convs = []
for kernel_size in kernel_sizes:
for map_n in range(maps_per_kernel):
conv = keras.layers.Conv1D(filters=50,
kernel_size=kernel_size,
padding='valid',
activation='relu',
kernel_initializer='glorot_normal',
strides=1)(input)
conv_drop = keras.layers.Dropout(0.1)(conv)
max_pool = keras.layers.MaxPooling1D(3)(conv_drop)
convs.append(max_pool)
merge = keras.layers.Concatenate(axis=1)(convs)
mix = keras.layers.Conv1D(filters=150,
kernel_size=3,
padding='valid',
activation='relu',
kernel_initializer='glorot_normal',
strides=1)(merge)
max = keras.layers.MaxPooling1D(3)(mix)
lstm = keras.layers.Bidirectional(keras.layers.LSTM(50, return_sequences=False,
dropout=0.15, recurrent_dropout=0.15, implementation=0))(max)
dense = keras.layers.Dense(60, activation='relu')(lstm)
drop = keras.layers.Dropout(0.5)(dense)
output = keras.layers.Dense(2, activation='sigmoid')(drop)
model = tf.keras.Model(inputs=input, outputs=output)
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def create_model_conv():
model = Sequential([
keras.layers.Input(shape=(120, 20)),
keras.layers.Conv1D(filters=50,
kernel_size=5,
padding='valid',
activation='relu',
kernel_initializer='glorot_normal',
strides=1),
keras.layers.Dropout(0.1),
keras.layers.Conv1D(filters=50,
kernel_size=3,
padding='valid',
activation='relu',
kernel_initializer='glorot_normal',
strides=1),
keras.layers.Dropout(0.1),
keras.layers.Conv1D(filters=150,
kernel_size=3,
padding='valid',
activation='relu',
kernel_initializer='glorot_normal',
strides=1),
keras.layers.Dropout(0.1),
keras.layers.MaxPooling1D(pool_size=2),
keras.layers.Flatten(),
keras.layers.Dense(40, activation='relu'),
keras.layers.Dropout(0.5),
keras.layers.Dense(2, activation='sigmoid')])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
def create_model_conv_lstm():
model = Sequential([
keras.layers.Input(shape=(120, 20)),
keras.layers.Conv1D(filters=50,
kernel_size=5,
padding='valid',
activation='relu',
kernel_initializer='glorot_normal',
strides=1),
keras.layers.Dropout(0.1),
keras.layers.Conv1D(filters=50,
kernel_size=3,
padding='valid',
activation='relu',
kernel_initializer='glorot_normal',
strides=1),
keras.layers.Dropout(0.1),
keras.layers.Conv1D(filters=150,
kernel_size=3,
padding='valid',
activation='relu',
kernel_initializer='glorot_normal',
strides=1),
keras.layers.Dropout(0.1),
keras.layers.MaxPooling1D(pool_size=2),
keras.layers.Bidirectional(keras.layers.LSTM(60, return_sequences=False,
dropout=0.15, recurrent_dropout=0.15, implementation=0)),
keras.layers.Dense(40, activation='relu'),
keras.layers.Dropout(0.5),
keras.layers.Dense(2, activation='sigmoid')])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
def create_model_lstm():
#### needs uniform hot vector matrix
model = Sequential([
keras.layers.Input(shape=(120, 20)),
keras.layers.Masking(mask_value=0., input_shape=(120, 20)),
keras.layers.Bidirectional(keras.layers.LSTM(60, return_sequences=False,
dropout=0.15, recurrent_dropout=0.15, implementation=0)),
keras.layers.Dense(40, activation='relu'),
keras.layers.Dropout(0.5),
keras.layers.Dense(2, activation='sigmoid')])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
def create_model_embedded_lstm():
#### needs uniform hot vector matrix
model = Sequential([
keras.layers.Embedding(21,128),
keras.layers.Bidirectional(keras.layers.LSTM(64, return_sequences=False, dropout=0.15,
recurrent_dropout=0.15, implementation=0)),
keras.layers.Dropout(0.3),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(2, activation='sigmoid')])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
def create_model_sequential_lstm():
#### needs uniform hot vector matrix
model = Sequential([
keras.layers.Input(shape=(18, 20)),
keras.layers.Bidirectional(keras.layers.LSTM(60, return_sequences=True,
dropout=0.15, recurrent_dropout=0.15, implementation=0)),
keras.layers.Dropout(0.5),
keras.layers.TimeDistributed(keras.layers.Dense(1, activation='sigmoid'))])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
return model