Keras模型给出TypeError:只有大小为1的数组可以转换为Python标量 [英] Keras Model giving TypeError: only size-1 arrays can be converted to Python scalars

问题描述

我正在训练一个模型来制作图像蒙版.该错误不断弹出，我无法确定原因.帮助将不胜感激.

I'm training a model to produce image masks. This error keeps popping up, and I can not determine the cause. Help would be appreciated.

错误声明:

File "--\Users\-----\Anaconda3\lib\site-packages\keras\initializers.py", line 209, in __call__
scale /= max(1., float(fan_in + fan_out) / 2)
TypeError: only size-1 arrays can be converted to Python scalars

在线搜索，当普通列表与numpy函数一起使用时会发生此错误，但在我的情况下，使用的数组是numpy数组.下面，我附上了代码.

Researching online, this error occurs when normal lists are used with numpy functions, but in my case, the arrays used are numpy arrays. Below, I've attached the code.

import cv2
import glob
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist
from keras import backend as K
K.set_image_dim_ordering('tf')
np.random.seed(123)  # for reproducibility

image_list = [] 
test_list = []

for filename in glob.glob("image/*.jpg*"): 
    im = cv2.imread(filename)
    im_r = cv2.resize(im,(200, 200), interpolation = cv2.INTER_AREA) 
    image_list.append(im_r)

for filename in glob.glob("test/*.png*"): 
    im = cv2.imread(filename)
    im_r = cv2.resize(im,(200, 200), interpolation = cv2.INTER_AREA) 
    im_r = np.ravel(im_r)
    test_list.append(im_r)

x_data = np.array(image_list)
y_data = np.array(test_list)
x_data = x_data.astype("float32")
y_data = y_data.astype("float32")
x_data /= 255
y_data /= 255

X_train = x_data
Y_train = y_data

model = Sequential()
model.add(Convolution2D(32, 5, 5, activation='relu', input_shape=(200, 200, 3)))
model.add(MaxPooling2D(pool_size=(2,2))) 
model.add(Convolution2D(32, 5, 5, activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2))) 
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2))) 
model.add(Dropout(0.25))
model.add(Flatten()) 
model.add(Dense(128, activation='relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(Y_train[0], activation='sigmoid'))
print('hello')

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
loss = acc = 0
while acc < 0.9999:
    model.fit(X_train, Y_train, batch_size=32, nb_epoch=10, verbose=1)
    loss, acc = model.evaluate(X_train, Y_train, verbose=1)
model.save("model_state_no_mapping")

推荐答案

问题出在模型的最后一层.

The problem is in the last layer of your model.

更改

model.add(Dense(Y_train[0], activation='sigmoid')) 

到

model.add(Dense(Y_train.shape[0], activation='sigmoid'))

此外，在较新版本的Keras中，建议使用Conv2D层而不是旧的Convolution2D.

Also, in newer versions of Keras it is recommended to use Conv2D layer instead of old Convolution2D.

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