def main():
gpus = tf.config.experimental.list_physical_devices("GPU")
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
except RuntimeError as e:
print(e)
exit(-1)
data_root = tf.keras.utils.get_file(
'flower_photos',
'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
untar=True)
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
str(data_root),
validation_split=0.2,
subset="training",
seed=123,
image_size=(224, 224),
batch_size=32)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
str(data_root),
validation_split=0.2,
subset="validation",
seed=123,
image_size=(224, 224),
batch_size=32)
class_names = np.array(train_ds.class_names)
# create direction for saving weights
if not os.path.exists("save_weights"):
os.makedirs("save_weights")
# # create model
model = AlexNet()
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['acc'])
history = model.fit(train_ds, epochs=10, validation_data=(val_ds))
model.save("./save_weights/AlexNet_model")
# 评估模型
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
plt.show()
def __init__(self):
self._input_shape = (224, 224, 3)
self._output_dim = 2
model = AlexNet(self._input_shape, self._output_dim).get_model()
optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=optimizer,
metrics=["accuracy"])
reduce_lro_n_plat = ReduceLROnPlateau(monitor='val_loss',
factor=0.8,
patience=10,
verbose=1,
mode='auto',
min_delta=0.0001,
cooldown=5,
min_lr=1e-10)
early = EarlyStopping(monitor="val_loss", mode="min", patience=20)
data_gen = ImageDataGenerator()
train_it = data_gen.flow_from_directory('data/all/train/',
target_size=(224, 224))
val_it = data_gen.flow_from_directory('data/all/validation/',
target_size=(224, 224))
callbacks_list = [early, reduce_lro_n_plat]
try:
model.fit(train_it,
batch_size=32,
epochs=10000,
validation_data=val_it,
callbacks=callbacks_list,
verbose=1)
except KeyboardInterrupt:
pass
model.save_weights("data/model.h5")
tfjs.converters.save_keras_model(model, "data/model")
