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# Copyright (c) 2015. Mount Sinai School of Medicine
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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import keras
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Flatten, Dropout
from keras.layers.embeddings import Embedding
import theano
theano.config.exception_verbosity = 'high'
def make_network(
input_size,
embedding_input_dim=None,
embedding_output_dim=None,
layer_sizes=[100],
activation="relu",
init="lecun_uniform",
loss="mse",
output_activation="sigmoid",
dropout_probability=0.0,
model=None,
optimizer=None):
if model is None:
model = Sequential()
if optimizer is None:
optimizer = keras.optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-6)
if embedding_input_dim:
if not embedding_output_dim:
raise ValueError(
"Both embedding_input_dim and embedding_output_dim must be set")
model.add(Embedding(
input_dim=embedding_input_dim,
output_dim=embedding_output_dim,
init=init))
model.add(Flatten())
input_size = input_size * embedding_output_dim
layer_sizes = (input_size,) + tuple(layer_sizes)
for i, dim in enumerate(layer_sizes):
if i == 0:
# input is only conceptually a layer of the network,
# don't need to actually do anything
continue
previous_dim = layer_sizes[i - 1]
# hidden layer fully connected layer
model.add(
Dense(
input_dim=previous_dim,
output_dim=dim,
init=init))
model.add(Activation(activation))
if dropout_probability > 0:
model.add(Dropout(dropout_probability))
# output
model.add(Dense(
input_dim=layer_sizes[-1],
output_dim=1,
init=init))
model.add(Activation(output_activation))
model.compile(loss=loss, optimizer=optimizer)
return model
def make_hotshot_network(
peptide_length=9,
layer_sizes=[500],
activation="relu",
init="lecun_uniform",
loss="mse",
output_activation="sigmoid",
dropout_probability=0.0,
optimizer=None):
return make_network(
input_size=peptide_length * 20,
layer_sizes=layer_sizes,
activation=activation,
init=init,
loss=loss,
output_activation=output_activation,
dropout_probability=dropout_probability,
optimizer=optimizer)
def make_embedding_network(
peptide_length=9,
embedding_input_dim=20,
embedding_output_dim=20,
layer_sizes=[500],
activation="relu",
init="lecun_uniform",
loss="mse",
output_activation="sigmoid",
dropout_probability=0.0,
optimizer=None):
return make_network(
input_size=peptide_length,
embedding_input_dim=embedding_input_dim,
embedding_output_dim=embedding_output_dim,
layer_sizes=layer_sizes,
activation=activation,
init=init,
loss=loss,
output_activation=output_activation,
dropout_probability=dropout_probability,
optimizer=optimizer)