报错，已将num-class改成了100
报错如下
Traceback (most recent call last):
File "E:\data\all\train.py", line 151, in <module>
main()
File "E:\data\all\train.py", line 100, in main
model.load_weights(pre_weights_path)
File "C:\Users\ASUS\AppData\Roaming\Python\Python39\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "E:\python\lib\site-packages\tensorflow\python\training\saving\saveable_object_util.py", line 139, in restore
raise ValueError(
ValueError: Received incompatible tensor with shape (4096, 5) when attempting to restore variable with shape (4096, 100) and name dense_2/kernel.
代码如下
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
plt.ion()
import numpy as np
import tensorflow as tf
import json
import os
import glob
from keras import layers, models
def AlexNet_pytorch(im_height=224, im_width=224, num_classes=1000):
# tensorflow中的tensor通道排序是NHWC
input_image = layers.Input(shape=(im_height, im_width, 3), dtype="float32") # output(None, 224, 224, 3)
x = layers.ZeroPadding2D(((2, 1), (2, 1)))(input_image) # output(None, 227, 227, 3)
x = layers.Conv2D(64, kernel_size=11, strides=4, activation="relu")(x) # output(None, 55, 55, 64)
x = layers.MaxPool2D(pool_size=3, strides=2)(x) # output(None, 27, 27, 64)
x = layers.Conv2D(192, kernel_size=5, padding="same", activation="relu")(x) # output(None, 27, 27, 192)
x = layers.MaxPool2D(pool_size=3, strides=2)(x) # output(None, 13, 13, 128)
x = layers.Conv2D(384, kernel_size=3, padding="same", activation="relu")(x) # output(None, 13, 13, 384)
x = layers.Conv2D(256, kernel_size=3, padding="same", activation="relu")(x) # output(None, 13, 13, 256)
x = layers.Conv2D(256, kernel_size=3, padding="same", activation="relu")(x) # output(None, 13, 13, 256)
x = layers.MaxPool2D(pool_size=3, strides=2)(x) # output(None, 6, 6, 256)
x = layers.Flatten()(x) # output(None, 6*6*256)
x = layers.Dropout(0.5)(x)
x = layers.Dense(4096, activation="relu")(x) # output(None, 4096)
x = layers.Dropout(0.5)(x)
x = layers.Dense(4096, activation="relu")(x) # output(None, 4096)
x = layers.Dense(num_classes)(x) # output(None, 100)
predict = layers.Softmax()(x)
model = models.Model(inputs=input_image, outputs=predict)
return model
def main():
data_root = os.path.abspath(os.path.join(os.getcwd(), "../..")) # get data root path
image_path = os.path.join(data_root, "amini", "D:/amini/images-ok/") # flower data set path
train_dir = os.path.join(image_path, "train")
validation_dir = os.path.join(image_path, "val")
assert os.path.exists(train_dir), "cannot find {}".format(train_dir)
assert os.path.exists(validation_dir), "cannot find {}".format(validation_dir)
# create direction for saving weights
if not os.path.exists("save_weights"):
os.makedirs("save_weights")
im_height = 224
im_width = 224
batch_size = 60
epochs = 10
def pre_function(img: np.ndarray):
# from PIL import Image as im
# import numpy as np
# img = im.open('test.jpg')
# img = np.array(img).astype(np.float32) 图像归一化
img = img / 225.
img = img - [0.485, 0.456, 0.406] #均值
img = img / [0.229, 0.224, 0.225] #方差
return img
# data generator with data augmentation
train_image_generator = ImageDataGenerator(horizontal_flip=True,
preprocessing_function=pre_function)
validation_image_generator = ImageDataGenerator(preprocessing_function=pre_function)
train_data_gen = train_image_generator.flow_from_directory(directory=train_dir,
batch_size=batch_size,
shuffle=True,
target_size=(im_height, im_width),
class_mode='categorical')
total_train = train_data_gen.n
# get class dict
class_indices = train_data_gen.class_indices
# transform value and key of dict
inverse_dict = dict((val, key) for key, val in class_indices.items())
# write dict into json file
json_str = json.dumps(inverse_dict, indent=4)
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)
val_data_gen = validation_image_generator.flow_from_directory(directory=validation_dir,
batch_size=batch_size,
shuffle=False,
target_size=(im_height, im_width),
class_mode='categorical')
total_val = val_data_gen.n
print("using {} images for training, {} images for validation.".format(total_train,
total_val))
model = AlexNet_pytorch(im_height=im_height, im_width=im_width, num_classes=100)
pre_weights_path = 'D:/amini/ckpt/pretrain_weights.ckpt'
assert len(glob.glob(pre_weights_path+"*")), "cannot find {}".format(pre_weights_path)
model.load_weights(pre_weights_path)
for layer_t in model.layers:
if 'conv2d' in layer_t.name:
layer_t.trainable = False
model.summary()
# using keras high level api for training
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False),
metrics=["accuracy"])
callbacks = [tf.keras.callbacks.ModelCheckpoint(filepath='./save_weights/myAlex.h5',
save_best_only=True,
save_weights_only=True,
monitor='val_loss')]
# tensorflow2.1 recommend to using fit
history = model.fit(x=train_data_gen,
steps_per_epoch=total_train // batch_size,
epochs=epochs,
validation_data=val_data_gen,
validation_steps=total_val // batch_size,
callbacks=callbacks)
# plot loss and accuracy image
history_dict = history.history
train_loss = history_dict["loss"]
train_accuracy = history_dict["accuracy"]
val_loss = history_dict["val_loss"]
val_accuracy = history_dict["val_accuracy"]
# figure 1
plt.figure()
plt.plot(range(epochs), train_loss, label='train_loss')
plt.plot(range(epochs), val_loss, label='val_loss')
plt.legend()
plt.xlabel('epochs')
plt.ylabel('loss')
# figure 2
plt.figure()
plt.plot(range(epochs), train_accuracy, label='train_accuracy')
plt.plot(range(epochs), val_accuracy, label='val_accuracy')
plt.legend()
plt.xlabel('epochs')
plt.ylabel('accuracy')
plt.show()
if __name__ == '__main__':
main()
