from keras import optimizers, losses, activations, models
from keras.layers import Dense, Input, Dropout, Convolution1D, MaxPool1D, GlobalMaxPool1D, GlobalAveragePooling1D, \
concatenate, SpatialDropout1D, TimeDistributed, Bidirectional, LSTM
from keras_contrib.layers import CRF
from utils import WINDOW_SIZE
def get_model():
nclass = 5
inp = Input(shape=(3000, 1))
img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp)
img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = GlobalMaxPool1D()(img_1)
img_1 = Dropout(rate=0.01)(img_1)
dense_1 = Dropout(rate=0.01)(Dense(64, activation=activations.relu, name="dense_1")(img_1))
dense_1 = Dropout(rate=0.05)(Dense(64, activation=activations.relu, name="dense_2")(dense_1))
dense_1 = Dense(nclass, activation=activations.softmax, name="dense_3")(dense_1)
model = models.Model(inputs=inp, outputs=dense_1)
opt = optimizers.Adam(0.001)
model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc'])
model.summary()
return model
def get_base_model():
inp = Input(shape=(3000, 1))
img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp)
img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = MaxPool1D(pool_size=2)(img_1)
img_1 = SpatialDropout1D(rate=0.01)(img_1)
img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
img_1 = GlobalMaxPool1D()(img_1)
img_1 = Dropout(rate=0.01)(img_1)
dense_1 = Dropout(0.01)(Dense(64, activation=activations.relu, name="dense_1")(img_1))
base_model = models.Model(inputs=inp, outputs=dense_1)
opt = optimizers.Adam(0.001)
base_model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc'])
#model.summary()
return base_model
def get_model_cnn():
nclass = 5
seq_input = Input(shape=(None, 3000, 1))
base_model = get_base_model()
# for layer in base_model.layers:
# layer.trainable = False
encoded_sequence = TimeDistributed(base_model)(seq_input)
encoded_sequence = SpatialDropout1D(rate=0.01)(Convolution1D(128,
kernel_size=3,
activation="relu",
padding="same")(encoded_sequence))
encoded_sequence = Dropout(rate=0.05)(Convolution1D(128,
kernel_size=3,
activation="relu",
padding="same")(encoded_sequence))
#out = TimeDistributed(Dense(nclass, activation="softmax"))(encoded_sequence)
out = Convolution1D(nclass, kernel_size=3, activation="softmax", padding="same")(encoded_sequence)
model = models.Model(seq_input, out)
model.compile(optimizers.Adam(0.001), losses.sparse_categorical_crossentropy, metrics=['acc'])
model.summary()
return model
def get_model_lstm():
nclass = 5
seq_input = Input(shape=(None, 3000, 1))
base_model = get_base_model()
for layer in base_model.layers:
layer.trainable = False
encoded_sequence = TimeDistributed(base_model)(seq_input)
encoded_sequence = Bidirectional(LSTM(100, return_sequences=True))(encoded_sequence)
encoded_sequence = Dropout(rate=0.5)(encoded_sequence)
encoded_sequence = Bidirectional(LSTM(100, return_sequences=True))(encoded_sequence)
#out = TimeDistributed(Dense(nclass, activation="softmax"))(encoded_sequence)
out = Convolution1D(nclass, kernel_size=1, activation="softmax", padding="same")(encoded_sequence)
model = models.Model(seq_input, out)
model.compile(optimizers.Adam(0.001), losses.sparse_categorical_crossentropy, metrics=['acc'])
model.summary()
return model
def get_model_cnn_crf(lr=0.001):
nclass = 5
seq_input = Input(shape=(None, 3000, 1))
base_model = get_base_model()
# for layer in base_model.layers:
# layer.trainable = False
encoded_sequence = TimeDistributed(base_model)(seq_input)
encoded_sequence = SpatialDropout1D(rate=0.01)(Convolution1D(128,
kernel_size=3,
activation="relu",
padding="same")(encoded_sequence))
encoded_sequence = Dropout(rate=0.05)(Convolution1D(128,
kernel_size=3,
activation="linear",
padding="same")(encoded_sequence))
#out = TimeDistributed(Dense(nclass, activation="softmax"))(encoded_sequence)
# out = Convolution1D(nclass, kernel_size=3, activation="linear", padding="same")(encoded_sequence)
crf = CRF(nclass, sparse_target=True)
out = crf(encoded_sequence)
model = models.Model(seq_input, out)
model.compile(optimizers.Adam(lr), crf.loss_function, metrics=[crf.accuracy])
model.summary()
return model
Datasets
Models
