from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten, LSTM, MaxPooling2D, Dropout, TimeDistributed, ConvLSTM2D
def assign_model_settings(num_labels,sparse_targets):
if num_labels <= 2:
loss_type = 'binary_crossentropy'
activation_output = 'sigmoid' # binary = "sigmoid"; multiple classification = "softmax"
else:
loss_type = 'categorical_crossentropy'
activation_output = 'softmax' # binary = "sigmoid"; multiple classification = "softmax"
if sparse_targets:
loss_type = 'sparse_categorical_crossentropy' # if data have mutiple labels which are only integer encoded, *not* one hot encoded.
return loss_type,activation_output
def buildmodel(model_type,num_labels,frame_width,timesteps,num_features,color_scale,lstm_cells,feature_map_filters,kernel_size,pool_size,dense_hidden_units,activation_output):
if 'lstm' == model_type.lower():
model = Sequential()
model.add(LSTM(lstm_cells,return_sequences=True,input_shape=(frame_width,num_features)))
model.add(LSTM(lstm_cells,return_sequences=True))
elif 'cnn' == model_type.lower():
model = Sequential()
# 4x8 time-frequency filter (goes along both time and frequency axes)
model.add(Conv2D(feature_map_filters, kernel_size=kernel_size, activation='relu',input_shape=(frame_width*timesteps,num_features,color_scale)))
#non-overlapping pool_size 3x3
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.25))
model.add(Dense(dense_hidden_units))
elif 'cnnlstm' == model_type.lower():
cnn = Sequential()
cnn.add(Conv2D(feature_map_filters, kernel_size=kernel_size, activation='relu'))
#non-overlapping pool_size 3x3
cnn.add(MaxPooling2D(pool_size=pool_size))
cnn.add(Dropout(0.25))
cnn.add(Flatten())
#prepare stacked LSTM
model = Sequential()
model.add(TimeDistributed(cnn,input_shape=(timesteps,frame_width,num_features,color_scale)))
model.add(LSTM(lstm_cells,return_sequences=True))
model.add(LSTM(lstm_cells,return_sequences=True))
model.add(Flatten())
model.add(Dense(num_labels,activation=activation_output))
return model
