我已经微调了VGG16模型,并以最佳精度保存了模型。我的keras代码是-
#Input size
rows = 125
column = 125
channels = 3 #RGB
#Resizing
def read_images(src):
#read every image and resize every image to same size
img = Image.open(src)
#print(img.size)
#method = Image.NEAREST if img.size == (rows,column) else Image.ANTIALIAS
im_rz = img.resize((rows,column), Image.ANTIALIAS)
#im_rz = resize_image_to_square(img, new_size = (rows,column))
#im_rz = ImageOps.fit(img, (rows,column), method = method)
return im_rz
#Conversion of image to np
l = LabelEncoder()
y_all = l.fit_transform(y_all)
y_all = np_utils.to_categorical(y_all)
#print(l.classes_)
#x_train, x_valid, y_train, y_valid = test_train_split(x_all, y_all, test_size = 0.2, random_state = 42, stratify = y_all)
x_train, x_test, y_train, y_test = train_test_split(x_all, y_all, test_size = 0.2, random_state = 42, stratify = y_all)
batch_size = 32
nb_classes = 3
epochs = 300
x_train = np.array(x_train).astype('float32')
x_test = np.array(x_test).astype('float32')
y_train = np.array(y_train).astype('float32')
y_test = np.array(y_test).astype('float32')
x_train /= 255.
x_test /= 255.
#model
vgg = VGG16(weights='imagenet', include_top=False, input_shape=(125, 125, 3))
# Freeze the layers except the last 4 layers
for layer in vgg.layers[:-4]:
layer.trainable = False
# Check the trainable status of the individual layers
for layer in vgg.layers:
print(layer, layer.trainable)
model = Sequential()
# Add the vgg convolutional base model
model.add(vgg_conv)
# Add new layers
model.add(layers.Flatten())
model.add(layers.Dense(1024, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(3, activation='softmax'))
print(model.summary())
opt = keras.optimizers.RMSProp(lr=1e-4)
# #Training the model using optimizer
model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])
#Save the best model obtained during training
#from keras.callbacks import ReduceLROnPlateau
#lr_reduce = ReduceLROnPlateau(monitor='val_acc', factor=0.1, epsilon=0.0001, patience=1, verbose=1)
filepath = "weights.h5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=0, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
#data generation and fitting
data_generation = ImageDataGenerator(
rotation_range = 45, #Makes sense to keep the rotation between 0 and 10, we don't want to rotate it a lot
width_shift_range = 0.0,
height_shift_range = 0.0,
horizontal_flip=True, #Random rotations, might help
vertical_flip=True,
zoom_range = 0.15
)
#data_generation.fit(x_train)
#Model fitting!
#tbCallBack = keras.callbacks.TensorBoard(log_dir="./Graph", histogram_freq = 0, write_graph = True, write_images = True)
#callbacks_list.append(tbCallBack)
model_param=model.fit_generator(data_generation.flow(x_train, y_train,batch_size=batch_size),
steps_per_epoch=x_train.shape[0] // batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
validation_steps=x_test.shape[0],
callbacks=callbacks_list)
这是模型的摘要
Layer (type) Output Shape Param #
=================================================================
vgg16 (Model) (None, 7, 7, 512) 14714688
_________________________________________________________________
flatten_1 (Flatten) (None, 25088) 0
_________________________________________________________________
dense_1 (Dense) (None, 1024) 25691136
_________________________________________________________________
dropout_1 (Dropout) (None, 1024) 0
_________________________________________________________________
dense_2 (Dense) (None, 3) 3075
=================================================================
Total params: 40,408,899
Trainable params: 32,773,635
Non-trainable params: 7,635,264
_________________________________________________________________
我已使用此脚本将其从.h5转换为.pb文件,以在C ++中使用。 https://github.com/bitbionic/keras-to-tensorflow/blob/master/k2tf_convert.py
我使用了类似于此代码的C ++代码- https://github.com/bitbionic/keras-to-tensorflow/blob/master/main.cpp
在C ++中执行代码时,出现以下错误-
Running model failed: Invalid argument: Matrix size-incompatible: In[0]: [1,1152], In[1]: [4608,512]
[[Node: dense_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/cpu:0"](flatten_1/Reshape, dense_1/kernel/read)]]
如何删除此错误,并且pb文件创建不正确?
答案 0 :(得分:0)
问题可能是由于将图像尺寸调整为125 * 125后再将其馈送到VGG16网络。它需要224 * 224的图像尺寸,否则在训练时图像尺寸可能会减小为零。将图片尺寸保持为224 * 224可消除所有错误,并允许以更大的批量尺寸训练模型。