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 = Xception(
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/Xception/xception_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/Xception/xception_sgd_1.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op,
loss="sparse_categorical_crossentropy",
import tensorflow as tf
from tensorflow.keras.applications import Xception
from tensorflow.keras.utils import multi_gpu_model
import numpy as np
import datetime
num_samples = 100
height = 71
width = 71
num_classes = 100
start1 = datetime.datetime.now()
with tf.device('/gpu:0'):
model = Xception(weights=None,
input_shape=(height, width, 3),
classes=num_classes)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
# Generate dummy data.
x = np.random.random((num_samples, height, width, 3))
y = np.random.random((num_samples, num_classes))
model.fit(x, y, epochs=3, batch_size=16)
model.save('my_model_h5')
end1 = datetime.datetime.now()
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 = Xception(
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/Xception/xception_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/Xception/xception_rmsprop_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/Xception/xception_rmsprop_1.h5')
num_classes = 100
start = datetime.datetime.now()
with tf.device('/gpu:0'):
model = Xception(weights=None,
input_shape=(height, width, 3),
classes=num_classes)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
# Generate dummy data.
x = np.random.random((num_samples, height, width, 3))
y = np.random.random((num_samples, num_classes))
model.fit(x, y, epochs=3, batch_size=16)
model.save('my_model.h5')
end = datetime.datetime.now()
time_delta = end - start
print('GPU 처리시간 : ', time_delta)
start = datetime.datetime.now()
with tf.device('/cpu:0'):
model = Xception(weights=None,
input_shape=(height, width, 3),
classes=num_classes)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
# Generate dummy data.
x = np.random.random((num_samples, height, width, 3))
y = np.random.random((num_samples, num_classes))
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 = Xception(
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/Xception/xception_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/Xception/xception_adadelta_1.h5'
mc = ModelCheckpoint(path, monitor='val_loss', verbose=1, save_best_only=True)
model.compile(optimizer=op,
loss="sparse_categorical_crossentropy",