x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], aaa)
print(x_train.shape, y_train.shape) # (3628, 128, 862, 1) (3628,)
print(x_test.shape, y_test.shape) # (908, 128, 862, 1) (908,)
model = NASNetMobile(
include_top=True,
input_shape=(128,862,1),
classes=2,
pooling=None,
weights=None,
)
model.summary()
# model.trainable = False
model.save('C:/nmb/nmb_data/h5/5s/Nasnet/nasnet_sgd_1.h5')
# 컴파일, 훈련
op = SGD(lr=1e-2)
batch_size = 4
es = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True, verbose=1)
lr = ReduceLROnPlateau(monitor='val_loss', vactor=0.5, patience=10, verbose=1)
path = 'C:/nmb/nmb_data/h5/5s/Nasnet/nasnet_sgd_1.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op, loss="sparse_categorical_crossentropy", metrics=['acc'])
history = model.fit(x_train, y_train, epochs=1000, batch_size=batch_size, validation_split=0.2, callbacks=[es, lr, mc])
# 평가, 예측
# model = load_model('C:/nmb/nmb_data/h5/5s/Nasnet/nasnet_sgd_1.h5')
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], aaa)
print(x_train.shape, y_train.shape) # (3628, 128, 862, 1) (3628,)
print(x_test.shape, y_test.shape) # (908, 128, 862, 1) (908,)
model = NASNetMobile(
include_top=True,
input_shape=(128,862,1),
classes=2,
pooling=None,
weights=None,
)
model.summary()
# model.trainable = False
model.save('C:/nmb/nmb_data/h5/5s/Nasnet/nasnet_adadelta_1.h5')
# 컴파일, 훈련
op = Adadelta(lr=1e-3)
batch_size = 4
es = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True, verbose=1)
lr = ReduceLROnPlateau(monitor='val_loss', vactor=0.5, patience=10, verbose=1)
path = 'C:/nmb/nmb_data/h5/5s/Nasnet/nasnet_adadelta_1.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op, loss="sparse_categorical_crossentropy", metrics=['acc'])
history = model.fit(x_train, y_train, epochs=1000, batch_size=batch_size, validation_split=0.2, callbacks=[es, lr, mc])
# 평가, 예측
# model = load_model('C:/nmb/nmb_data/h5/5s/Nasnet/nasnet_adadelta_1.h5')
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], aaa)
print(x_train.shape, y_train.shape) # (3628, 128, 862, 1) (3628,)
print(x_test.shape, y_test.shape) # (908, 128, 862, 1) (908,)
model = NASNetMobile(
include_top=True,
input_shape=(128, 862, 1),
classes=2,
pooling=None,
weights=None,
)
model.summary()
# model.trainable = False
model.save('C:/nmb/nmb_data/h5/5s/Nasnet/nasnet_rmsprop_1.h5')
# 컴파일, 훈련
op = RMSprop(lr=1e-3)
batch_size = 4
es = EarlyStopping(monitor='val_loss',
patience=20,
restore_best_weights=True,
verbose=1)
lr = ReduceLROnPlateau(monitor='val_loss', vactor=0.5, patience=10, verbose=1)
path = 'C:/nmb/nmb_data/h5/5s/Nasnet/nasnet_rmsprop_1.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op,
loss="sparse_categorical_crossentropy",