def create_model(sequence_length,
feature_dim,
RANDOM_STATE,
hd,
cell_type = 'RNN',
dropout = 0.2):
noLayers = int(np.sum(hd != 0))
#sequence_length, feature_dim = seqs.X_train[0].shape
if cell_type == 'LSTM':
RNNobj = LSTM
elif cell_type == 'RNN':
RNNobj = SimpleRNN
callbacks = [ModelCheckpoint(
filepath = 'weights.{epoch:02d}-{val_acc:.2f}.hdf5',
monitor = 'val_acc',
save_best_only = True)]
#create model
model = Sequential()
if noLayers == 1:
model.add(RNNobj(input_shape = (sequence_length, feature_dim),
units = hd[0]))
model.add(Dropout(dropout))
elif noLayers == 2:
model.add(RNNobj(return_sequences = True,
input_shape = (sequence_length, feature_dim),
units = hd[0]))
model.add(Dropout(dropout))
model.add(RNNobj(hd[1]))
model.add(Dropout(dropout))
elif noLayers == 3:
model.add(RNNobj(return_sequences = True,
input_shape = (sequence_length, feature_dim),
units = hd[0]))
model.add(Dropout(dropout))
model.add(RNNobj(return_sequences = True, units = hd[1]))
model.add(Dropout(dropout))
model.add(RNNobj(hd[2]))
model.add(Dropout(dropout))
model.add(Dense(feature_dim, activation='sigmoid'))
if feature_dim == 2:
loss = 'binary_crossentropy'
else:
loss = 'categorical_crossentropy'
model.compile(loss = loss,
optimizer = 'adam',
metrics = ['accuracy'])
model.RANDOM_STATE = RANDOM_STATE
return model
def create_model(hd=[10, 0, 0],
sequence_length=30,
feature_dim=4,
output_dim=2,
RANDOM_STATE=10,
cell_type='RNN',
dropout=0.2):
noLayers = int(np.sum(hd != 0))
#sequence_length, feature_dim = seqs.X_train[0].shape
if cell_type == 'LSTM':
RNNobj = LSTM
elif cell_type == 'RNN':
RNNobj = SimpleRNN
#create model
model = Sequential()
if noLayers == 1:
model.add(
RNNobj(input_shape=(sequence_length, feature_dim), units=hd[0]))
model.add(Dropout(dropout))
elif noLayers == 2:
model.add(
RNNobj(return_sequences=True,
input_shape=(sequence_length, feature_dim),
units=hd[0]))
model.add(Dropout(dropout))
model.add(RNNobj(hd[1]))
model.add(Dropout(dropout))
elif noLayers == 3:
model.add(
RNNobj(return_sequences=True,
input_shape=(sequence_length, feature_dim),
units=hd[0]))
model.add(Dropout(dropout))
model.add(RNNobj(return_sequences=True, units=hd[1]))
model.add(Dropout(dropout))
model.add(RNNobj(hd[2]))
model.add(Dropout(dropout))
model.add(Dense(output_dim, activation='sigmoid'))
if output_dim == 2:
loss = 'binary_crossentropy'
else:
loss = 'categorical_crossentropy'
model.compile(loss=loss, optimizer='adam', metrics=['accuracy'])
model.RANDOM_STATE = RANDOM_STATE
return model
def create_model(sequence_length,
feature_dim,
RANDOM_STATE,
UNITS,
cell_type='RNN',
dropout=0.2):
'''
Arguments:
sequence_length - how many trials back to look
feature_dim - how big the vocabulary space is
cell_type - whether to include simple RNN or LSTM cells
drouput - 0 <= rate < 1
UNITS - array specifying how many units per layer, if value is 0 then
we don't want that as a layer: e.g. [10 10 10] is 3 layers with 10 units
each. [20 50 0] is a 2-layer network with 20 units in the first layer and
50 units in the second layer.
output:
compiled model, with ADAM optimizer with default learning rate
'''
noLayers = int(np.sum(UNITS != 0))
if cell_type == 'LSTM':
RNNobj = LSTM
elif cell_type == 'RNN':
RNNobj = SimpleRNN
#create model
model = Sequential()
if noLayers == 1:
model.add(
RNNobj(input_shape=(sequence_length, feature_dim), units=UNITS[0]))
model.add(Dropout(dropout))
elif noLayers == 2:
model.add(
RNNobj(return_sequences=True,
input_shape=(sequence_length, feature_dim),
units=UNITS[0]))
model.add(Dropout(dropout))
model.add(RNNobj(UNITS[1]))
model.add(Dropout(dropout))
elif noLayers == 3:
model.add(
RNNobj(return_sequences=True,
input_shape=(sequence_length, feature_dim),
units=UNITS[0]))
model.add(Dropout(dropout))
model.add(RNNobj(return_sequences=True, units=UNITS[1]))
model.add(Dropout(dropout))
model.add(RNNobj(UNITS[2]))
model.add(Dropout(dropout))
#give probabilities
model.add(Dense(feature_dim, activation='sigmoid'))
if feature_dim == 2:
loss = 'binary_crossentropy'
else:
loss = 'categorical_crossentropy'
model.compile(loss=loss, optimizer='adam', metrics=['accuracy'])
model.RANDOM_STATE = RANDOM_STATE
return model
