def build_rlstm2(input_dim,
h0_dim,
h1_dim,
output_dim=1,
lstm_init='zero',
lr=.001,
base_name='rlstm',
add_input_noise=True,
add_target_noise=False):
model = Sequential()
if add_input_noise:
model.add(GaussianNoise(.1, input_shape=(None, input_dim)))
model.add(
RLSTM(input_dim,
h0_dim,
h1_dim,
output_dim,
init=lstm_init,
W_h0_regularizer=l2(0.0005),
W_h1_regularizer=l2(0.0005),
return_sequences=True))
# if add_target_noise:
# model.add(GaussianNoise(5.))
model.compile(loss="mse", optimizer=RMSprop(lr=lr))
model.base_name = base_name
yaml_string = model.to_yaml()
# print(yaml_string)
with open(model_savedir + model.base_name + '.yaml', 'w') as f:
f.write(yaml_string)
return model
def build_rlstm(input_dim, h0_dim=40, h1_dim=None, output_dim=1,
rec_layer_type=ReducedLSTMA, rec_layer_init='zero', fix_b_f=False,
layer_type=TimeDistributedDense, lr=.001, base_name='rlstm',
add_input_noise=True, add_target_noise=True):
model = Sequential()
if add_input_noise:
model.add(GaussianNoise(.1, input_shape=(None, input_dim)))
model.add(layer_type(h0_dim, input_dim=input_dim,
init='uniform_small',
W_regularizer=l2(0.0005),
activation='tanh'))
if h1_dim is not None:
model.add(layer_type(h1_dim,
init='uniform_small',
W_regularizer=l2(0.0005),
activation='tanh'))
model.add(rec_layer_type(output_dim, init=rec_layer_init, fix_b_f=fix_b_f, return_sequences=True))
if add_target_noise:
model.add(GaussianNoise(5.))
model.compile(loss="mse", optimizer=RMSprop(lr=lr))
model.base_name = base_name
yaml_string = model.to_yaml()
# print(yaml_string)
with open(model_savedir + model.base_name+'.yaml', 'w') as f:
f.write(yaml_string)
return model
def build_rnn(in_dim, out_dim, h0_dim, h1_dim=None, layer_type=LSTM, return_sequences=False):
model = Sequential()
model.add(layer_type(h0_dim, input_shape=(None, in_dim), return_sequences=(h1_dim is not None or return_sequences)))
if h1_dim is not None:
model.add(layer_type(h1_dim, return_sequences=return_sequences))
if return_sequences:
model.add(TimeDistributedDense(out_dim))
else:
model.add(Dense(out_dim))
model.add(Activation("linear"))
model.compile(loss="mse", optimizer="rmsprop")
return model
def build_rlstm2(input_dim, h0_dim, h1_dim, output_dim=1,
lstm_init='zero', lr=.001, base_name='rlstm',
add_input_noise=True, add_target_noise=False):
model = Sequential()
if add_input_noise:
model.add(GaussianNoise(.1, input_shape=(None, input_dim)))
model.add(RLSTM(input_dim, h0_dim, h1_dim, output_dim, init=lstm_init,
W_h0_regularizer=l2(0.0005), W_h1_regularizer=l2(0.0005), return_sequences=True))
# if add_target_noise:
# model.add(GaussianNoise(5.))
model.compile(loss="mse", optimizer=RMSprop(lr=lr))
model.base_name = base_name
yaml_string = model.to_yaml()
# print(yaml_string)
with open(model_savedir + model.base_name+'.yaml', 'w') as f:
f.write(yaml_string)
return model
def build_rlstm(input_dim,
h0_dim=40,
h1_dim=None,
output_dim=1,
rec_layer_type=ReducedLSTMA,
rec_layer_init='zero',
fix_b_f=False,
layer_type=TimeDistributedDense,
lr=.001,
base_name='rlstm',
add_input_noise=True,
add_target_noise=True):
model = Sequential()
if add_input_noise:
model.add(GaussianNoise(.1, input_shape=(None, input_dim)))
model.add(
layer_type(h0_dim,
input_dim=input_dim,
init='uniform_small',
W_regularizer=l2(0.0005),
activation='tanh'))
if h1_dim is not None:
model.add(
layer_type(h1_dim,
init='uniform_small',
W_regularizer=l2(0.0005),
activation='tanh'))
model.add(
rec_layer_type(output_dim,
init=rec_layer_init,
fix_b_f=fix_b_f,
return_sequences=True))
if add_target_noise:
model.add(GaussianNoise(5.))
model.compile(loss="mse", optimizer=RMSprop(lr=lr))
model.base_name = base_name
yaml_string = model.to_yaml()
# print(yaml_string)
with open(model_savedir + model.base_name + '.yaml', 'w') as f:
f.write(yaml_string)
return model
def build_reduced_lstm(input_dim, h0_dim=40, h1_dim=None, output_dim=1,
rec_layer_type=ReducedLSTMA, rec_layer_init='uniform',
layer_type=TimeDistributedDense, lr=.001, base_name='rlstm'):
model = Sequential()
model.add(layer_type(h0_dim, input_dim=input_dim,
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
if h1_dim is not None:
model.add(layer_type(h1_dim,
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
# model.add(LSTM(h0_dim,
# input_dim=input_dim,
# init='uniform',
# inner_activation='sigmoid',
# return_sequences=True))
# model.add(Dropout(0.4))
# if h1_dim is not None:
# model.add(LSTM(h1_dim,
# init='uniform',
# inner_activation='sigmoid',
# return_sequences=True))
# model.add(Dropout(0.4))
model.add(rec_layer_type(output_dim, init=rec_layer_init, return_sequences=True))
model.compile(loss="mse", optimizer=RMSprop(lr=lr))
model.base_name = base_name
yaml_string = model.to_yaml()
# print(yaml_string)
with open(model_savedir + model.base_name+'.yaml', 'w') as f:
f.write(yaml_string)
return model
def train_model(dataset, h0_dim, h1_dim, out_dim):
X_train, y_train, X_test, y_test = dataset
batch_size = 128
nb_epoch = 100
model = Sequential()
model.add(
RNN(h0_dim,
input_shape=(None, X_train.shape[-1]),
return_sequences=True))
model.add(TimeDistributedDense(out_dim))
model.add(Activation("linear"))
model.compile(loss="mse", optimizer="rmsprop")
#model.get_config(verbose=1)
#yaml_string = model.to_yaml()
#with open('ifshort_mlp.yaml', 'w') as f:
# f.write(yaml_string)
early_stopping = EarlyStopping(monitor='val_loss', patience=10)
checkpointer = ModelCheckpoint(filepath="/tmp/ifshort_rnn_weights.hdf5",
verbose=1,
save_best_only=True)
model.fit(X_train,
y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
show_accuracy=False,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[early_stopping, checkpointer])
def train_model(dataset, h0_dim, h1_dim, y_dim):
X_train, y_train, X_test, y_test = dataset
batch_size = 512
nb_epoch = 100
model = Sequential()
model.add(Dense(h0_dim, input_shape=(X_train.shape[1],),
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
model.add(Dense(h1_dim,
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
model.add(Dense(y_dim,
init='uniform',
W_regularizer=l2(0.0005)))
rms = RMSprop()
sgd = SGD(lr=0.01, decay=1e-4, momentum=0.6, nesterov=False)
model.compile(loss='mse', optimizer=sgd)
#model.get_config(verbose=1)
#yaml_string = model.to_yaml()
#with open('ifshort_mlp.yaml', 'w') as f:
# f.write(yaml_string)
early_stopping = EarlyStopping(monitor='val_loss', patience=10)
checkpointer = ModelCheckpoint(filepath="/tmp/ifshort_mlp_weights.hdf5", verbose=1, save_best_only=True)
model.fit(X_train, y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
show_accuracy=False,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[early_stopping, checkpointer])
def build_reduced_lstm(input_dim,
h0_dim=40,
h1_dim=None,
output_dim=1,
rec_layer_type=ReducedLSTMA,
rec_layer_init='uniform',
layer_type=TimeDistributedDense,
lr=.001,
base_name='rlstm'):
model = Sequential()
model.add(
layer_type(h0_dim,
input_dim=input_dim,
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
if h1_dim is not None:
model.add(
layer_type(h1_dim,
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
# model.add(LSTM(h0_dim,
# input_dim=input_dim,
# init='uniform',
# inner_activation='sigmoid',
# return_sequences=True))
# model.add(Dropout(0.4))
# if h1_dim is not None:
# model.add(LSTM(h1_dim,
# init='uniform',
# inner_activation='sigmoid',
# return_sequences=True))
# model.add(Dropout(0.4))
model.add(
rec_layer_type(output_dim, init=rec_layer_init, return_sequences=True))
model.compile(loss="mse", optimizer=RMSprop(lr=lr))
model.base_name = base_name
yaml_string = model.to_yaml()
# print(yaml_string)
with open(model_savedir + model.base_name + '.yaml', 'w') as f:
f.write(yaml_string)
return model
def build_mlp(in_dim, out_dim, h0_dim, h1_dim, learning_rate=.01):
model = Sequential()
model.add(Dense(h0_dim, input_shape=(in_dim,),
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
model.add(Dense(h1_dim,
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
model.add(Dense(out_dim,
init='uniform',
W_regularizer=l2(0.0005)))
# sgd = SGD(lr=0.01, decay=1e-4, momentum=0.6, nesterov=False)
sgd = SGD(lr=learning_rate, decay=1e-14, momentum=0.6, nesterov=False)
model.compile(loss='mse', optimizer=sgd)
return model
def train_model(dataset, h0_dim, h1_dim, out_dim):
X_train, y_train, X_test, y_test = dataset
batch_size = 128
nb_epoch = 100
model = Sequential()
model.add(RNN(h0_dim, input_shape=(None, X_train.shape[-1]), return_sequences=True))
model.add(TimeDistributedDense(out_dim))
model.add(Activation("linear"))
model.compile(loss="mse", optimizer="rmsprop")
#model.get_config(verbose=1)
#yaml_string = model.to_yaml()
#with open('ifshort_mlp.yaml', 'w') as f:
# f.write(yaml_string)
early_stopping = EarlyStopping(monitor='val_loss', patience=10)
checkpointer = ModelCheckpoint(filepath="/tmp/ifshort_rnn_weights.hdf5", verbose=1, save_best_only=True)
model.fit(X_train, y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
show_accuracy=False,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[early_stopping, checkpointer])
def build_mlp(in_dim, out_dim, h0_dim, h1_dim, optimizer='rmsprop'):
model = Sequential()
model.add(Dense(h0_dim, input_shape=(in_dim,),
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
model.add(Dense(h1_dim,
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
model.add(Dense(out_dim,
init='uniform',
W_regularizer=l2(0.0005)
))
# sgd = SGD(lr=0.01, decay=1e-4, momentum=0.6, nesterov=False)
# sgd = SGD(lr=learning_rate, decay=1e-24, momentum=0.6, nesterov=False)
model.compile(loss='mse', optimizer=optimizer)
# model.get_config(verbose=1)
yaml_string = model.to_yaml()
with open(model_savedir + 'mlp.yaml', 'w') as f:
f.write(yaml_string)
return model
def build_mlp(in_dim, out_dim, h0_dim, h1_dim, optimizer='rmsprop'):
model = Sequential()
model.add(
Dense(h0_dim,
input_shape=(in_dim, ),
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
model.add(
Dense(h1_dim,
init='uniform',
W_regularizer=l2(0.0005),
activation='relu'))
model.add(Dense(out_dim, init='uniform', W_regularizer=l2(0.0005)))
# sgd = SGD(lr=0.01, decay=1e-4, momentum=0.6, nesterov=False)
# sgd = SGD(lr=learning_rate, decay=1e-24, momentum=0.6, nesterov=False)
model.compile(loss='mse', optimizer=optimizer)
# model.get_config(verbose=1)
yaml_string = model.to_yaml()
with open(model_savedir + 'mlp.yaml', 'w') as f:
f.write(yaml_string)
return model
## convert class vectors to binary class matrices
#Y_train = np_utils.to_categorical(y_train, nb_classes)
#Y_test = np_utils.to_categorical(y_test, nb_classes)
from pylearn2.datasets.if_monthly import IFMonthlyLong
train = IFMonthlyLong(target_type='ASV', gain_range=[0, 10], which_set='train')
X_train = train.X
y_train = train.y
test = IFMonthlyLong(target_type='ASV', gain_range=[0, 10], which_set='test')
X_test = test.X
y_test = test.y
model = Sequential()
model.add(Dense(80, input_shape=(79,),
init='uniform',
# init=lambda shape: uniform(shape, scale=0.05),
W_regularizer=l2(0.0005),
activation='relu'))
#model.add(Dropout(0.2))
model.add(Dense(40,
init='uniform',
# init=lambda shape: uniform(shape, scale=0.05),
W_regularizer=l2(0.0005),
activation='relu'))
#model.add(Dropout(0.2))
model.add(Dense(1,
init='uniform',
# init=lambda shape: uniform(shape, scale=0.005),
W_regularizer=l2(0.0005)))
rms = RMSprop()
#Y_train = np_utils.to_categorical(y_train, nb_classes)
#Y_test = np_utils.to_categorical(y_test, nb_classes)
from pylearn2.datasets.if_monthly import IFMonthlyLong
train = IFMonthlyLong(target_type='ASV', gain_range=[0, 10], which_set='train')
X_train = train.X
y_train = train.y
test = IFMonthlyLong(target_type='ASV', gain_range=[0, 10], which_set='test')
X_test = test.X
y_test = test.y
model = Sequential()
model.add(
Dense(
80,
input_shape=(79, ),
init='uniform',
# init=lambda shape: uniform(shape, scale=0.05),
W_regularizer=l2(0.0005),
activation='relu'))
#model.add(Dropout(0.2))
model.add(
Dense(
40,
init='uniform',
# init=lambda shape: uniform(shape, scale=0.05),
W_regularizer=l2(0.0005),
activation='relu'))
#model.add(Dropout(0.2))
model.add(
Dense(
1,