model.add(
BatchNormalization(batch_input_shape=(None, None, 513), mode=0, axis=2))
#model.add(BatchNormalization(input_shape = (50,513), epsilon=1e-6, weights=None))
#model.add(LSTM(input_dim=513, input_length=None, output_dim=513, return_sequences=True))
model.add(
LSTM(input_dim=513,
input_length=None,
output_dim=1026,
return_sequences=True))
#model.add(Bidirectional(LSTM(input_dim=1026, input_length=None, output_dim=1026, return_sequences=True)))
# output layer
model.add(TimeDistributed(Dense(output_dim=1026)))
model.add(Activation("sigmoid"))
model.compile(optimizer='RMSprop', loss='binary_crossentropy')
train_list = ni.prep_CHiME2_lists(base_dir, mask_type=mask_type)
print len(train_list)
num_proc_files = 0
start_from_file = 0
while num_proc_files < 1000:
print "Running experiment."
start_time = time.strftime('%Y-%m-%d %T')
print start_time
# create new experiment folder
print "Creating new folder for this experiment in:", save_dir
newexp_folder_path = save_dir + '/' + "exp_" + start_time + '/'
os.makedirs(newexp_folder_path)
keras_inputs, keras_targets, num_proc_files = ni.prep_data(
train_list, input_shape=(500, 50, 513), start=start_from_file)
start_from_file = start_from_file + num_proc_files
# define sequential model
model = Sequential()
# the 1st LSTM layer
model.add(BatchNormalization(input_shape= (50,513), mode=0,axis=2))
#model.add(BatchNormalization(input_shape = (50,513), epsilon=1e-6, weights=None))
#model.add(LSTM(input_dim=513, input_length=None, output_dim=513, return_sequences=True))
model.add(Bidirectional(LSTM(input_dim=513, input_length=None, output_dim=1026, return_sequences=True),merge_mode='concat'))
model.add(Bidirectional(LSTM(input_dim=513, input_length=None, output_dim=1026, return_sequences=True),merge_mode='concat'))
#model.add(Bidirectional(LSTM(input_dim=1026, input_length=None, output_dim=1026, return_sequences=True)))
# output layer
model.add(TimeDistributed(Dense(output_dim=1026)))
model.add(Activation("sigmoid"))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer='RMSprop', loss='binary_crossentropy')
train_list = ni.prep_CHiME2_lists(base_dir, mask_type='ideal_amplitude')
print len(train_list)
num_proc_files =0
start_from_file = 0
while num_proc_files<200:
print "Running experiment."
start_time = time.strftime('%Y-%m-%d %T')
print start_time
# create new experiment folder
print "Creating new folder for this experiment in:", save_dir
newexp_folder_path = save_dir + '/' + "exp_" + start_time + '/'
os.makedirs(newexp_folder_path)
keras_inputs, keras_targets, num_proc_files = ni.prep_data_log(train_list, input_shape=(200, 50, 513), start=start_from_file)
start_from_file = start_from_file + num_proc_files
print keras_inputs.shape, num_proc_files