def create_model(shape):
from keras import layers
# from keras import models
from keras import optimizers
# from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from tensorflow.keras import Model
# from tensorflow.keras.initializers import glorot_uniform
from tensorflow.keras.regularizers import l2
# from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import Xception
model = Xception(input_shape = (shape, shape, 3), include_top = False, weights = 'imagenet')
x = model.output
x = layers.AveragePooling2D(pool_size = (2, 2))(x)
x = layers.Dense(32, activation = 'relu')(x)
x = layers.Flatten()(x)
# spostato dopo Flatten
x = layers.Dropout(0.1)(x)
# a caso
x = layers.Dense(128)(x)
x = layers.Dense(4, activation = 'softmax', kernel_regularizer = l2(.0005))(x)
model = Model(inputs = model.inputs, outputs = x)
opt = optimizers.SGD(lr = 0.0001, momentum = .9)
model.compile(loss = 'categorical_crossentropy', optimizer = opt, metrics = ['accuracy'])
return model
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",
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_sgd_1.h5')
# model.load_weights('C:/nmb/nmb_data/h5/5s/Xception/xception_sgd_1.h5')
result = model.evaluate(x_test, y_test, batch_size=8)
print("loss : {:.5f}".format(result[0]))
print("acc : {:.5f}".format(result[1]))
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()
def loadModel(mode, modelWeights, organ, modelType):
"""
Load model and compile it
Input training or inference mode, model weights and type of model
Return model
"""
# Load model input configuration
modelInputConfig = loadModelInputConf(organ)
# Get values
useChannels = modelInputConfig.useChannels
useClasses = modelInputConfig.useClasses
useResolution = modelInputConfig.useResolution
# Define model
if modelType == 'ResNet101':
model = ResNet101(include_top=True,
weights=modelWeights,
input_shape=(useResolution[0], useResolution[1],
useChannels),
classes=useClasses)
elif modelType == 'SEResNet101':
mySEResNet = AllSEResNets.SEResNet101
model = mySEResNet(include_top=True,
weights=modelWeights,
input_shape=(useResolution[0], useResolution[1],
useChannels),
classes=useClasses)
elif modelType == 'SEResNet154':
mySEResNet = AllSEResNets.SEResNet154
model = mySEResNet(include_top=True,
weights=modelWeights,
input_shape=(useResolution[0], useResolution[1],
useChannels),
classes=useClasses)
# elif modelType == 'SEInceptionResNetV2':
# mySEInceptionResNet = AllSEInceptionResNets.SEInceptionResNetV2
# model = mySEInceptionResNet(include_top=True, weights=modelWeights, input_shape=(
# useResolution[0], useResolution[1], useChannels), classes=useClasses)
elif modelType == 'EfficientNetB4':
model = EfficientNetB4(include_top=True,
weights=modelWeights,
input_shape=(useResolution[0], useResolution[1],
useChannels),
classes=useClasses,
classifier_activation="softmax")
elif modelType == 'Xception':
model = Xception(include_top=True,
weights=modelWeights,
input_shape=(useResolution[0], useResolution[1],
useChannels),
classes=useClasses)
elif modelType == 'ResNet101V2':
model = ResNet101V2(include_top=True,
weights=modelWeights,
input_shape=(useResolution[0], useResolution[1],
useChannels),
classes=useClasses,
classifier_activation="softmax")
elif modelType == 'ResNet152V2':
model = ResNet152V2(include_top=True,
weights=modelWeights,
input_shape=(useResolution[0], useResolution[1],
useChannels),
classes=useClasses,
classifier_activation="softmax")
elif modelType == 'InceptionResNetV2':
model = InceptionResNetV2(include_top=True,
weights=modelWeights,
input_shape=(useResolution[0],
useResolution[1], useChannels),
classes=useClasses,
classifier_activation="softmax")
elif modelType == 'ResNet50V2':
model = ResNet50V2(include_top=True,
weights=modelWeights,
input_shape=(useResolution[0], useResolution[1],
useChannels),
classes=useClasses,
classifier_activation="softmax")
elif modelType == 'NASNetLarge':
model = NASNetLarge(include_top=True,
weights=modelWeights,
input_shape=(useResolution[0], useResolution[1],
useChannels),
classes=useClasses)
else:
raise ValueError('The selected model could not be found')
if mode == 'training':
print('Loaded model ' + modelType + ' for training, no weights loaded')
# Add reglizarization if needed
# model = addRegularization(model, tf.keras.regularizers.l2(0.0000))
if mode == 'inference':
print('Loaded model ' + modelType + ' for inference, weights loaded.')
# Do not add regularization
model.compile(
optimizer='adam',
loss='categorical_crossentropy',
# metrics=['accuracy']
metrics=[
'accuracy',
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall(),
tf.keras.metrics.AUC()
],
weighted_metrics=[
'accuracy',
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall(),
tf.keras.metrics.AUC()
])
return model
