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train.py
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train.py
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from tensorflow import keras
def make_model(input_shape, num_classes):
'''
model's architecture
:param input_shape:
:param num_classes:
:return:
'''
input_layer = keras.layers.Input(input_shape)
conv1 = keras.layers.Conv1D(filters=64, kernel_size=3, padding="same")(input_layer)
conv1 = keras.layers.BatchNormalization()(conv1)
conv1 = keras.layers.ReLU()(conv1)
conv2 = keras.layers.Conv1D(filters=64, kernel_size=3, padding="same")(conv1)
conv2 = keras.layers.BatchNormalization()(conv2)
conv2 = keras.layers.ReLU()(conv2)
conv3 = keras.layers.Conv1D(filters=64, kernel_size=3, padding="same")(conv2)
conv3 = keras.layers.BatchNormalization()(conv3)
conv3 = keras.layers.ReLU()(conv3)
gap = keras.layers.GlobalAveragePooling1D()(conv3)
output_layer = keras.layers.Dense(num_classes, activation="softmax")(gap)
return keras.models.Model(inputs=input_layer, outputs=output_layer)
def create_model(x_train):
'''
prep model
:param x_train:
:return:
'''
return make_model(input_shape=x_train.shape[1:])
def set_callbacks(trained_model_path):
'''
set callbacks for training process
:param trained_model_path:
:return:
'''
callbacks = [
keras.callbacks.ModelCheckpoint(
trained_model_path, save_best_only=True, monitor="val_loss"
),
keras.callbacks.ReduceLROnPlateau(
monitor="val_loss", factor=0.5, patience=20, min_lr=0.0001
),
keras.callbacks.EarlyStopping(monitor="val_loss", patience=50, verbose=1),
]
return callbacks
def train(trained_model_path, model, x_train, y_train, epochs=200, batch_size=64):
'''
run training process
:param trained_model_path:
:param model:
:param x_train:
:param y_train:
:param epochs:
:param batch_size:
:return:
'''
# epochs = 500
# batch_size = 32
callbacks = set_callbacks(trained_model_path)
model.compile(
optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["sparse_categorical_accuracy"],
)
history = model.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks,
validation_split=0.2,
verbose=1,
)
return history