def get_model(train=True):
if Path('model.h5').is_file():
return load_model('model.h5')
datagen = ImageDataGenerator(
rotation_range=10,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.1,
zoom_range=0.2,
horizontal_flip=False,
preprocessing_function=gen_preprocess,
fill_mode='nearest')
data_generator = datagen.flow_from_directory(
directory='train_data/',
target_size=IMG_SIZE,
class_mode='categorical')
print(data_generator.classes)
validgen = ImageDataGenerator(preprocessing_function=gen_preprocess)
valid_generator = validgen.flow_from_directory(
directory='valid_data/',
target_size=IMG_SIZE,
class_mode='categorical',
shuffle=False
)
test_generator = validgen.flow_from_directory(
directory='test_data/',
target_size=IMG_SIZE,
class_mode='categorical',
shuffle=False
)
model = SqueezeNet()
print(model.summary())
x = Convolution2D(4, (1, 1), padding='same', name='conv11')(model.layers[-5].output)
x = Activation('relu', name='relu_conv11')(x)
x = GlobalAveragePooling2D()(x)
x = Activation('softmax')(x)
# x= Dense(4, activation='softmax')(x)
# x = Dense(4, activation='softmax')(model.layers[-2].output)
model = Model(model.inputs, x)
print(model.summary())
# Following is the original model I was training
# model = Sequential()
#
# model.add(Convolution2D(16, 3, 3,
# border_mode='same',
# input_shape=IMG_SHAPE))
# model.add(MaxPooling2D(pool_size=(3, 3)))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# model.add(Convolution2D(32, 3, 3,
# border_mode='same'))
# model.add(MaxPooling2D(pool_size=(3, 3)))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# model.add(Convolution2D(48, 3, 3,
# border_mode='same'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
# #
# model.add(Convolution2D(64, 3, 3,
# border_mode='same'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
# #
# model.add(Convolution2D(64, 3, 3,
# border_mode='same'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# # 1st Layer - Add a flatten layer
# model.add(Flatten())
#
# model.add(Dense(1164))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# model.add(Dense(128))
# model.add(Activation('tanh'))
# model.add(Dropout(0.2))
#
# # 2nd Layer - Add a fully connected layer
# model.add(Dense(50))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# model.add(Dense(10))
# model.add(Activation('relu'))
# model.add(Dropout(0.2))
#
# # 4th Layer - Add a fully connected layer
# model.add(Dense(4))
# # 5th Layer - Add a ReLU activation layer
# model.add(Activation('softmax'))
# TODO: Build a Multi-layer feedforward neural network with Keras here.
# TODO: Compile and train the model
filepath = "weights-improvement-{epoch:02d}-{loss:.2f}.hdf5"
callbacks = [
EarlyStopping(monitor='loss', min_delta=0.01, patience=2, verbose=1),
LambdaCallback(on_epoch_end=lambda batch,logs: evaluate_model(model, test_generator)),
ModelCheckpoint(filepath=filepath, monitor='loss', save_best_only=True, verbose=1),
]
model.compile(keras.optimizers.Adam(lr=0.0001), 'categorical_crossentropy', ['accuracy'])
model.fit_generator(data_generator, steps_per_epoch=400, epochs=30, verbose=1, callbacks=callbacks)
evaluate_model(model, test_generator)
model.save('model.h5', True)
return model
model = SqueezeNet(
include_top=False
) # Construct the feature extraction layers in squeezenet.
model = add_squeezenet_top(
model, args.classes,
False) # Add the classification layers to squeezenet.
data_set = Dataloader(args.data_base,
args.label_path) # Construct the Dataloader class
data, label = data_set.read_data(args.pos_neg_num,
args.target_img_size) # Read the data
X_train, X_test, y_train, y_test = train_test_split(
data, label, test_size=args.split_ratio, random_state=42)
# Split the data into training and validation
y_train = keras.utils.to_categorical(y_train, args.classes)
y_test = keras.utils.to_categorical(y_test, args.classes)
# Convert the label to one-hot label
batch_size = args.batch_epoch_size[
0] # Set the batch size of training, normally 16 or 32
nb_epoch = args.batch_epoch_size[1] # Set the epoch size of training
model.fit(X_train,
y_train,
batch_size=batch_size,
epochs=nb_epoch,
verbose=1,
validation_data=(X_test, y_test))
# Start training
model.save(args.model_path)
# Save the model to specific path
model = SqueezeNet()
#保存model部分 # save as JSON 保存模型的结构，而不包含其权重或配置信息
model_json = model.to_json()
# with open("model.json", "w") as json_file:
# json_file.write(model_json)
# del_json = model.to_json()
# json_file.write(model_json)
#只保存权重
model.save_weights("model.h5")
#保存model和权重
model.save('my_model.h5') # creates a HDF5 file 'my_model.h5'
del model # deletes the existing model
model = load_model(
'my_model.h5') # returns a compiled model identical to the previous one
#img = image.load_img(img_path, target_size=(224, 224)) # 加载图像，归一化大小
img = image.load_img('images/cat.jpeg', target_size=(227, 227))
x = image.img_to_array(img) # 序列化
x = np.expand_dims(x, axis=0) # 展开
x = preprocess_input(x) # 预处理到0～1
preds = model.predict(x) # 预测结果，1000维的向量
print(
'Predicted:',
decode_predictions(preds)) # decode_predictions 输出5个最高概率：(类名, 语义概念, 预测概率)