def model_256_128_64_2_100Ep(FrameSize, X, X_train, X_test, y_train, y_test):
model = Sequential()
# model.add(Embedding(2, 50, input_length=None))
# model.add(LSTM(256, return_sequences=True))
model.add(
LSTM(256,
input_shape=(FrameSize, X[0].shape[1]),
return_sequences=True,
recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.2))
model.add(Dense(64))
model.add(Dropout(0.2))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
epochs=100,
batch_size=128,
shuffle=True,
verbose=2,
validation_data=(X_test, y_test))
plot.plot(history, "One_256_128_64_2_100Ep")
ROC_PR.ROC(model, X_test, y_test, "One_256_128_64_2_100Ep")
def model_CNN_LSTM_limited_1(FrameSize, X, X_train, X_test, y_train, y_test,
epoch, earlyStopping, name):
print(X.shape)
print(FrameSize)
model = Sequential()
model.add(Dropout(0.43369355853937297))
model.add(
Conv1D(filters=5, kernel_size=4, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(
Conv1D(filters=7, kernel_size=7, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=7, padding='same'))
model.add(LSTM(398, return_sequences=True, recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.43369355853937297))
model.add(LSTM(106, return_sequences=True, recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.43369355853937297))
model.add(LSTM(475, return_sequences=True, recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.43369355853937297))
model.add(LSTM(264, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.43369355853937297))
model.add(Dense(352))
model.add(Dropout(0.43369355853937297))
model.add(Dense(378))
model.add(Dropout(0.43369355853937297))
model.add(Dense(7, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(
X_train,
y_train,
epochs=epoch,
batch_size=128,
# shuffle=True,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN_LSTM_limited_1.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
# plot_model(model, to_file='model_plot.png', show_shapes=True)
plot.plot(history, ("CNN_LSTM_limited_1" + name))
score = ROC_PR.ROC(model,
X_test,
y_test, ("CNN_LSTM_limited_1" + name),
True,
limited=True)
return score
def model_CNN_LSTM_random_data(FrameSize, X, X_train, X_test, y_train, y_test,
epoch, earlyStopping, name):
print(X.shape)
print(FrameSize)
model = Sequential()
model.add(Dropout(0.3311428861138142))
model.add(
Conv1D(filters=4, kernel_size=6, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(Dropout(0.3311428861138142))
model.add(
Conv1D(filters=7, kernel_size=4, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(Dropout(0.3311428861138142))
model.add(
Conv1D(filters=6, kernel_size=6, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(Dropout(0.3311428861138142))
model.add(
Conv1D(filters=4, kernel_size=4, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(LSTM(425, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3311428861138142))
model.add(LSTM(189, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3311428861138142))
model.add(LSTM(283, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3311428861138142))
model.add(LSTM(333, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.3311428861138142))
model.add(Dense(331))
model.add(Dropout(0.3311428861138142))
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
plot.plot(history, ("LRCN" + name))
score = ROC_PR.ROC(model, X_test, y_test, ("LRCN" + name), True)
return score, ROC_PR.ROC_Score(model, X_train, y_train, limited=False)
def model_CNN_LSTM_time(FrameSize, X, X_train, X_test, y_train, y_test, epoch,
earlyStopping, name):
print(X.shape)
print(X_train.shape)
print(X_test.shape)
print(FrameSize)
print(y_train.shape)
print(y_test.shape)
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1],
X_train.shape[2], 1)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2],
1)
# y_train = y_train.reshape(7060, 12, 1)
# y_test = y_test.reshape(785, 12, 1)
model = Sequential()
model.add(Dropout(0.1))
model.add(
TimeDistributed(
Conv1D(filters=8, kernel_size=3, activation='relu',
padding='same')))
model.add(TimeDistributed(MaxPooling1D(pool_size=3, padding='same')))
model.add(
TimeDistributed(
Conv1D(filters=8, kernel_size=3, activation='relu',
padding='same')))
model.add(TimeDistributed(MaxPooling1D(pool_size=3, padding='same')))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(518, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.1))
model.add(Dense(64))
model.add(Dropout(0.1))
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
plot.plot(history, ("LRCN" + name))
score = ROC_PR.ROC(model, X_test, y_test, ("LRCN" + name), True)
return score, ROC_PR.ROC_Score(model, X_train, y_train, limited=False)
def model_256_128_64_2_StateFul(FrameSize, X, X_train, X_test, y_train, y_test,
epoch, earlyStopping):
batch_size = 128
if len(X_train) // 128 != len(X_train) / 128:
tmp = X_train[0]
tmp2 = y_train[0]
for j in range(0, len(tmp2)):
tmp2[j] = 0
for j in range(0, len(tmp)):
for k in range(0, len(tmp[j])):
if tmp[j][k] == 1:
tmp[j][k] = 0
for j in range(0,
((((len(X_train) // 128) + 1) * 128) - len(X_train))):
X_train = np.append(X_train, np.expand_dims(tmp, axis=0), axis=0)
y_train = np.append(y_train, np.expand_dims(tmp2, axis=0), axis=0)
for j in range(0, ((((len(X_test) // 128) + 1) * 128) - len(X_test))):
X_test = np.append(X_test, np.expand_dims(tmp, axis=0), axis=0)
y_test = np.append(y_test, np.expand_dims(tmp2, axis=0), axis=0)
model = Sequential()
model.add(
LSTM(256,
batch_input_shape=(batch_size, FrameSize, X[0].shape[1]),
return_sequences=True,
stateful=True))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(128, return_sequences=False, stateful=True))
model.add(Dropout(0.2))
model.add(Dense(64))
model.add(Dropout(0.2))
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
print(model.summary())
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=batch_size,
shuffle=True,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/256_128_64_2_StateFul.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
plot.plot(history, "256_128_64_2_StateFul")
def model_lrcn_simple(FrameSize,
X,
X_train,
X_test,
y_train,
y_test,
epoch,
earlyStopping,
name,
limited=False):
print(X.shape)
print(FrameSize)
model = Sequential()
model.add(Dropout(0.2))
model.add(
Conv1D(filters=5, kernel_size=5, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
model.add(LSTM(256, return_sequences=True, recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.2))
model.add(Dense(128))
model.add(Dropout(0.2))
if limited:
model.add(Dense(7, activation='sigmoid'))
else:
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
plot.plot(history, ("LRCN" + name))
score = ROC_PR.ROC(model,
X_test,
y_test, ("LRCN" + name),
True,
limited=limited)
return score
def model_256_128_64_2BS(FrameSize, X, X_train, X_test, y_train, y_test,
epoch):
model = Sequential()
# model.add(Embedding(2, 50, input_length=None))
# model.add(LSTM(256, return_sequences=True))
model.add(
LSTM(256,
input_shape=(FrameSize, X[0].shape[1]),
return_sequences=True,
recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.2))
model.add(Dense(64))
model.add(Dropout(0.2))
model.add(Dense(2, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
verbose=2,
shuffle=True,
validation_data=(X_test, y_test),
callbacks=[
ModelCheckpoint('result/One_256_128_64_2BS.h5',
monitor='val_accuracy',
mode='max',
save_best_only=True)
])
# model.save_weights("result/One_256_128_64_2BS.h5")
plot.plot(history, "One_256_128_64_2BS")
ROC_PR.ROC(model, X_test, y_test, "One_256_128_64_2BS")
def model_CNN256_LSTM128_64_2(FrameSize, X, X_train, X_test, y_train, y_test,
epoch):
model = Sequential()
model.add(Dropout(0.2))
model.add(
Conv1D(filters=5, kernel_size=3, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=3))
model.add(LSTM(256, return_sequences=True, recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.2))
model.add(Dense(64))
model.add(Dropout(0.2))
model.add(Dense(2, activation='sigmoid'))
model.compile(loss='categorical_crossentropy',
optimizer='Adam',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
shuffle=True,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='accuracy',
mode='max',
save_best_only=True)
])
# plot_model(model, to_file='model_plot.png', show_shapes=True)
plot.plot(history, "One_CNN256_LSTM128_64_2")
ROC_PR.ROC(model, X_test, y_test, "One_CNN256_LSTM128_64_2", False)
def model_256_128_64_2(FrameSize, X, X_train, X_test, y_train, y_test, epoch,
earlyStopping):
model = Sequential()
model.add(
LSTM(256,
input_shape=(FrameSize, X[0].shape[1]),
return_sequences=True,
recurrent_dropout=0.3))
model.add(SpatialDropout1D(0.2))
model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.2))
model.add(Dense(64))
model.add(Dropout(0.2))
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
print(model.summary())
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
shuffle=True,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/256_128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
plot.plot(history, "256_128_64_2")
ROC_PR.ROC(model, X_test, y_test, "256_128_64_2", True)
def model_CNN256_LSTM128_64_2(FrameSize,
X,
X_train,
X_test,
y_train,
y_test,
epoch,
earlyStopping,
name,
dropout2_rate,
dense_1,
filterCNN,
kernelCNN,
LSTM1,
LSTM2,
recurrent_dropout,
limited=False):
print(X.shape)
print(FrameSize)
model = Sequential()
# model.add(TimeDistributed(Conv1D(filters=1, kernel_size=3, activation='relu', padding='same', input_shape=(FrameSize, X[0].shape[1], 1))))
# model.add(TimeDistributed(MaxPooling1D(pool_size=3)))
# model.add(TimeDistributed(Flatten()))
model.add(Dropout(dropout2_rate))
# model.add(Conv1D(filters=5, kernel_size=3, activation='relu', padding='same'))
model.add(
Conv1D(filters=filterCNN,
kernel_size=kernelCNN,
activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
# model.add(TimeDistributed(Flatten()))
# model.add(LSTM(256, return_sequences=True, recurrent_dropout=0.3))
model.add(
LSTM(LSTM1, return_sequences=True,
recurrent_dropout=recurrent_dropout))
model.add(SpatialDropout1D(dropout2_rate))
# model.add(LSTM(128, return_sequences=False, recurrent_dropout=0.3))
model.add(
LSTM(LSTM2,
return_sequences=False,
recurrent_dropout=recurrent_dropout))
model.add(Dropout(dropout2_rate))
# model.add(Dense(64))
model.add(Dense(dense_1))
model.add(Dropout(dropout2_rate))
if limited:
model.add(Dense(7, activation='sigmoid'))
else:
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(
X_train,
y_train,
epochs=epoch,
batch_size=128,
# shuffle=True,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
# plot_model(model, to_file='model_plot.png', show_shapes=True)
plot.plot(history, ("LRCN" + name))
score = ROC_PR.ROC(model,
X_test,
y_test, ("LRCN" + name),
True,
limited=limited)
return score
def model_shuffle_index_0(FrameSize, X, X_train, X_test, y_train, y_test,
epoch, earlyStopping, name, index):
print(X.shape)
print(FrameSize)
model = Sequential()
if index == 0:
model.add(Dropout(0.3975303416300372))
model.add(
Conv1D(filters=4, kernel_size=3, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
model.add(Dropout(0.3975303416300372))
model.add(
Conv1D(filters=6, kernel_size=3, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
model.add(Dropout(0.3975303416300372))
model.add(
Conv1D(filters=7, kernel_size=3, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
model.add(LSTM(107, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3975303416300372))
model.add(LSTM(161, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3975303416300372))
model.add(LSTM(386, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.3975303416300372))
model.add(Dense(90))
model.add(Dropout(0.3975303416300372))
model.add(Dense(503))
model.add(Dropout(0.3975303416300372))
model.add(Dense(319))
model.add(Dropout(0.3975303416300372))
model.add(Dense(151))
model.add(Dropout(0.3975303416300372))
model.add(Dense(12, activation='sigmoid'))
elif index == 1:
model.add(Dropout(0.1135579744586698))
model.add(
Conv1D(filters=4, kernel_size=5, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(LSTM(420, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.1135579744586698))
model.add(LSTM(67, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.1135579744586698))
model.add(LSTM(231, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.1135579744586698))
model.add(Dense(112))
model.add(Dropout(0.1135579744586698))
model.add(Dense(92))
model.add(Dropout(0.1135579744586698))
model.add(Dense(12, activation='sigmoid'))
elif index == 2:
model.add(Dropout(0.3211287914743064))
model.add(
Conv1D(filters=7, kernel_size=3, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=3, padding='same'))
model.add(Dropout(0.3211287914743064))
model.add(
Conv1D(filters=4, kernel_size=5, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(Dropout(0.3211287914743064))
model.add(
Conv1D(filters=5, kernel_size=4, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=4, padding='same'))
model.add(LSTM(404, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.3211287914743064))
model.add(Dense(69))
model.add(Dropout(0.3211287914743064))
model.add(Dense(70))
model.add(Dropout(0.3211287914743064))
model.add(Dense(171))
model.add(Dropout(0.3211287914743064))
model.add(Dense(453))
model.add(Dropout(0.3211287914743064))
model.add(Dense(12, activation='sigmoid'))
elif index == 3:
model.add(Dropout(0.3584182599371434))
model.add(
Conv1D(filters=8, kernel_size=5, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=5, padding='same'))
model.add(Dropout(0.3584182599371434))
model.add(
Conv1D(filters=7, kernel_size=5, activation='relu',
padding='same'))
model.add(MaxPooling1D(pool_size=5, padding='same'))
model.add(LSTM(438, return_sequences=True, recurrent_dropout=0.3))
model.add(Dropout(0.3584182599371434))
model.add(LSTM(500, return_sequences=False, recurrent_dropout=0.3))
model.add(Dropout(0.3584182599371434))
model.add(Dense(81))
model.add(Dropout(0.3584182599371434))
model.add(Dense(267))
model.add(Dropout(0.3584182599371434))
model.add(Dense(388))
model.add(Dropout(0.3584182599371434))
model.add(Dense(12, activation='sigmoid'))
model.compile(loss=masked_loss_function,
optimizer='Adam',
metrics=[masked_accuracy])
history = model.fit(X_train,
y_train,
epochs=epoch,
batch_size=128,
verbose=2,
validation_data=(X_test, y_test),
callbacks=[
earlyStopping,
ModelCheckpoint('result/CNN256_LSTM128_64_2.h5',
monitor='val_masked_accuracy',
mode='max',
save_best_only=True)
])
plot.plot(history, ("LRCN" + name))
score = ROC_PR.ROC(model, X_test, y_test, ("LRCN" + name), True)
return score, ROC_PR.ROC_Score(model, X_train, y_train, limited=False)
