def main(args):
args = parse_train_args(args)
X_train = np.load(args.X_train)
X_validate = np.load(args.X_validate)
y_train = np.load(args.y_train)
y_validate = np.load(args.y_validate)
model_save_path = args.model_save_path
example_shape = X_train.shape[1:]
input_layer = Input(shape=example_shape)
conv_1 = Conv2D(filters=40, kernel_size=3, padding='same', activation='relu')(input_layer)
pool_1 = MaxPool2D(pool_size=(2, 1))(conv_1)
conv_2 = Conv2D(filters=20, kernel_size=3, padding='same', activation='relu')(pool_1)
flatten = Flatten()(conv_2)
predictions = Dense(6, activation='softmax')(flatten)
model = Model(input_layer, predictions)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.summary())
batch_size = 10000
epochs = 100
callbacks = [EarlyStopping(monitor='val_loss', patience=3)]
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_validate, y_validate), callbacks=callbacks)
model.save(model_save_path)
def main(args):
args = parse_train_args(args)
X_train = np.load(args.X_train)
X_validate = np.load(args.X_validate)
y_train = np.load(args.y_train)
y_validate = np.load(args.y_validate)
model_save_path = args.model_save_path
tensorboard_output_dir = args.tensorboard_output_dir
def lr_schedule(epoch, lr):
if epoch > 50:
if epoch % 10 == 0:
return lr * 0.95
return lr
lr_callback = LearningRateScheduler(lr_schedule)
callbacks = [lr_callback,
EarlyStopping(monitor='val_loss', patience=3),
TensorBoard(log_dir=tensorboard_output_dir, write_images=True, write_grads=True, histogram_freq=5, batch_size=10000)]
input_shape = X_train.shape[1:]
num_output_classes = y_train.shape[1]
input_layer = Input(shape=input_shape)
conv_1 = Conv1D(filters=16, kernel_size=4, padding='same', activation='selu', kernel_regularizer=l2(1e-3))(input_layer)
pool_1 = MaxPooling1D(pool_size=(5), strides=1)(conv_1)
conv_2 = Conv1D(filters=32, kernel_size=4, padding='same', activation='selu', kernel_regularizer=l2(1e-3))(pool_1)
pool_2 = MaxPooling1D(pool_size=(4), strides=1)(conv_2)
conv_3 = Conv1D(filters=48, kernel_size=4, padding='same', activation='selu', kernel_regularizer=l2(1e-3))(pool_2)
pool_3 = MaxPooling1D(pool_size=(3), strides=1)(conv_3)
flatten = Flatten()(pool_3)
dn_1 = Dense(336, activation='selu')(flatten)
drop = Dropout(0.5)(dn_1)
predictions = Dense(num_output_classes, activation='softmax')(drop)
model = Model(input_layer, predictions)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.summary())
batch_size = 10000
epochs = 150
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_validate, y_validate), callbacks=callbacks)
model.save(model_save_path)
def main(args):
args = parse_train_args(args)
X_train = np.load(args.X_train)
X_valid = np.load(args.X_validate)
y_train = np.load(args.y_train)
y_valid = np.load(args.y_validate)
model_save_path = args.model_save_path
def lr_schedule(epoch, lr):
if epoch > 50:
if epoch % 5 == 0:
return lr * 0.95
return lr
lr_callback = LearningRateScheduler(lr_schedule)
callbacks = [lr_callback, EarlyStopping(monitor='val_loss', patience=3)]
input_shape = X_train.shape[1:]
num_output_classes = y_train.shape[1]
input_layer = Input(shape=input_shape)
conv_1 = Conv1D(filters=40,
kernel_size=3,
padding='same',
activation='relu',
kernel_regularizer=l2(0.01))(input_layer)
pool_1 = MaxPooling1D(pool_size=(2))(conv_1)
conv_2 = SeparableConv1D(filters=40,
kernel_size=3,
padding='same',
activation='relu',
kernel_regularizer=l2(0.01))(pool_1)
bn_1 = BatchNormalization()(conv_2)
flatten = Flatten()(bn_1)
predictions = Dense(num_output_classes, activation='softmax')(flatten)
model = Model(input_layer, predictions)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.summary())
batch_size = 10000
epochs = 150
model.fit(X_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(X_valid, y_valid),
callbacks=callbacks)
model.save(model_save_path)
def main(args):
args = parse_train_args(args)
X_train = np.load(args.X_train)
X_validate = np.load(args.X_validate)
y_train = np.load(args.y_train)
y_validate = np.load(args.y_validate)
model_save_path = args.model_save_path
def lr_schedule(epoch, lr):
if epoch > 50:
if epoch % 10 == 0:
return lr * 0.95
return lr
lr_callback = LearningRateScheduler(lr_schedule)
callbacks = [lr_callback, EarlyStopping(monitor='val_loss', patience=3)]
example_shape = X_train.shape[1:]
input_layer = Input(shape=example_shape)
conv_1 = Conv2D(filters=20,
kernel_size=3,
padding='same',
activation='relu')(input_layer)
conv_2 = Conv2D(filters=10,
kernel_size=3,
padding='same',
activation='relu')(conv_1)
bn_1 = BatchNormalization()(conv_2)
flatten = Flatten()(bn_1)
predictions = Dense(6, activation='softmax')(flatten)
model = Model(input_layer, predictions)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.summary())
batch_size = 10000
epochs = 150
model.fit(X_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(X_validate, y_validate),
callbacks=callbacks)
model.save(model_save_path)
def main(args):
args = parse_train_args(args)
X_train = np.load(args.X_train)
X_validate = np.load(args.X_validate)
y_train = np.load(args.y_train)
y_validate = np.load(args.y_validate)
model_save_path = args.model_save_path
input_shape = X_train.shape[1:]
num_output_classes = y_train.shape[1]
input_layer = Input(shape=input_shape)
conv_1 = Conv2D(filters=16,
kernel_size=3,
padding='same',
activation='selu')(input_layer)
pool_1 = MaxPooling2D(pool_size=(2, 1))(conv_1)
conv_2 = Conv2D(filters=32,
kernel_size=3,
padding='same',
activation='selu')(pool_1)
flatten = Flatten()(conv_2)
dense_1 = Dense(1042, activation='selu')(flatten)
dropout_1 = Dropout(0.25)(dense_1)
predictions = Dense(num_output_classes, activation='softmax')(dropout_1)
model = Model(input_layer, predictions)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.summary())
batch_size = 10000
epochs = 50
callbacks = [
EarlyStopping(monitor='val_loss', patience=3),
TensorBoard(log_dir='./logs', write_images=True, histogram_freq=0)
]
model.fit(X_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(X_validate, y_validate),
callbacks=callbacks)
model.save(model_save_path)
def main(args):
args = parse_train_args(args)
X_train = np.load(args.X_train)
X_validate = np.load(args.X_validate)
y_train = np.load(args.y_train)
y_validate = np.load(args.y_validate)
model_save_path = args.model_save_path
tensorboard_output_dir = args.tensorboard_output_dir
def lr_schedule(epoch, lr):
if epoch > 50:
if epoch % 10 == 0:
return lr * 0.95
return lr
lr_callback = LearningRateScheduler(lr_schedule)
callbacks = [lr_callback,
TensorBoard(log_dir=tensorboard_output_dir, write_images=True, write_grads=True, histogram_freq=5)]
input_shape = X_train.shape[1:]
num_output_classes = y_train.shape[1]
input_layer = Input(shape=input_shape)
conv_1 = Conv1D(filters=40, kernel_size=3, padding='same', activation='relu')(input_layer)
pool_1 = MaxPooling1D(pool_size=(2))(conv_1)
conv_2 = Conv1D(filters=40, kernel_size=3, padding='same', activation='relu')(pool_1)
pool_2 = MaxPooling1D(pool_size=(2))(conv_2)
conv_3 = Conv1D(filters=40, kernel_size=3, padding='same', activation='relu')(pool_2)
bn_1 = BatchNormalization()(conv_3)
flatten = Flatten()(bn_1)
predictions = Dense(num_output_classes, activation='softmax')(flatten)
model = Model(input_layer, predictions)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.summary())
batch_size = 10000
epochs = 50
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_validate, y_validate), callbacks=callbacks)
model.save(model_save_path)
def main(args):
args = parse_train_args(args)
global X_train, X_validate, y_train, y_validate
global tensorboard_output_dir
X_train = np.load(args.X_train)
X_validate = np.load(args.X_validate)
y_train = np.load(args.y_train)
y_validate = np.load(args.y_validate)
model_save_path = args.model_save_path
tensorboard_output_dir = args.tensorboard_output_dir
run_optimizer()
def main(args):
args = parse_train_args(args)
X_train = np.load(args.X_train)
X_validate = np.load(args.X_validate)
y_train = np.load(args.y_train)
y_validate = np.load(args.y_validate)
model_save_path = args.model_save_path
input_shape = X_train.shape[1:]
num_output_classes = y_train.shape[1]
input_layer = Input(shape=input_shape)
conv_1 = Conv2D(filters=10,
kernel_size=3,
padding='same',
activation='relu')(input_layer)
bn_1 = BatchNormalization()(conv_1)
conv_2 = Conv2D(filters=10,
kernel_size=3,
padding='same',
activation='relu')(bn_1)
drop_1 = Dropout(0.25)(conv_2)
flatten = Flatten()(drop_1)
predictions = Dense(num_output_classes, activation='softmax')(flatten)
model = Model(input_layer, predictions)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.summary())
batch_size = 10000
epochs = 100
callbacks = [EarlyStopping(monitor='val_loss', patience=3)]
model.fit(X_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(X_validate, y_validate),
callbacks=callbacks)
model.save(model_save_path)
