Question

我试图运行此程序，但出现错误

import sys
import os
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense, Activation
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras import callbacks
import time

start = time.time()

DEV = False
argvs = sys.argv
argc = len(argvs)

if argc > 1 and (argvs[1] == "--development" or argvs[1] == "-d"):
  DEV = True

if DEV:
  epochs = 2
else:
  epochs = 20

train_data_path = 'data/train'
validation_data_path = 'data/test'

"""
Parameters
"""
img_width, img_height = 150, 150
batch_size = 32
samples_per_epoch = 1000
validation_steps = 300
nb_filters1 = 32
nb_filters2 = 64
conv1_size = 3
conv2_size = 2
pool_size = 2
classes_num = 3
lr = 0.0004

model = Sequential()
model.add(Convolution2D(nb_filters1, conv1_size, conv1_size, border_mode ="same", input_shape=(img_width, img_height,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))

model.add(Convolution2D(nb_filters2, conv2_size, conv2_size, border_mode ="same"))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(pool_size, pool_size), dim_ordering='th'))

model.add(Flatten())
model.add(Dense(256))
model.add(Activation("relu"))
model.add(Dropout(0.5))
model.add(Dense(classes_num, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer=optimizers.RMSprop(lr=lr),
              metrics=['accuracy'])

train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_path,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
    validation_data_path,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical')

"""
Tensorboard log
"""
log_dir = './tf-log/'
tb_cb = callbacks.TensorBoard(log_dir=log_dir, histogram_freq=0)
cbks = [tb_cb]
print("testing purpose")
model.fit_generator(
    train_generator,
    samples_per_epoch=samples_per_epoch,
    epochs=epochs,
    validation_data=validation_generator,
    callbacks=cbks,
    validation_steps=validation_steps)

target_dir = './models/'
if not os.path.exists(target_dir):
  os.mkdir(target_dir)
model.save('./models/model.h5')
model.save_weights('./models/weights.h5')

#Calculate execution time
end = time.time()
dur = end-start

if dur<60:
    print("Execution Time:",dur,"seconds")
elif dur>60 and dur<3600:
    dur=dur/60
    print("Execution Time:",dur,"minutes")
else:
    dur=dur/(60*60)
    print("Execution Time:",dur,"hours")

错误：

Traceback (most recent call last):
  File "Training.py", line 95, in <module>
    validation_steps=validation_steps)
  File "/home/shawn-codoid/Desktop/python3/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/home/shawn-codoid/Desktop/python3/lib/python3.6/site-packages/keras/engine/training.py", line 1418, in fit_generator
    initial_epoch=initial_epoch)
  File "/home/shawn-codoid/Desktop/python3/lib/python3.6/site-packages/keras/engine/training_generator.py", line 217, in fit_generator
    class_weight=class_weight)
  File "/home/shawn-codoid/Desktop/python3/lib/python3.6/site-packages/keras/engine/training.py", line 1211, in train_on_batch
    class_weight=class_weight)
  File "/home/shawn-codoid/Desktop/python3/lib/python3.6/site-packages/keras/engine/training.py", line 789, in _standardize_user_data
    exception_prefix='target')
  File "/home/shawn-codoid/Desktop/python3/lib/python3.6/site-packages/keras/engine/training_utils.py", line 138, in standardize_input_data
    str(data_shape))
ValueError: Error when checking target: expected dense_2 to have shape (3,) but got array with shape (2,)

Answer 1

您的classes_num = 3，但是训练数据只有2个班级

我有这样的代码，但出现错误

1 个答案: