keras误差预测 [英] keras error on predict
问题描述
我正在尝试使用keras神经网络来识别绘制数字的画布图像并输出数字。我已经保存了神经网络，并使用django来运行Web界面。但是每当我运行它，我得到一个内部服务器错误和服务器端代码的错误。错误说异常：检查期间出现错误：expected dense_input_1具有形状（无，784），但具有数组（784,1）。我唯一的主要观点是从django.shortcuts导入render
从django.http导入HttpResponse
导入
I am trying to use a keras neural network to recognize canvas images of drawn digits and output the digit. I have saved the neural network and use django to run the web interface. But whenever I run it, I get an internal server error and an error on the server side code. The error says Exception: Error when checking : expected dense_input_1 to have shape (None, 784) but got array with shape (784, 1). My only main view is
from django.shortcuts import render
from django.http import HttpResponse
import StringIO
from PIL import Image
import numpy as np
import re
from keras.models import model_from_json
def home(request):
if request.method=="POST":
vari=request.POST.get("imgBase64","")
imgstr=re.search(r'base64,(.*)', vari).group(1)
tempimg = StringIO.StringIO(imgstr.decode('base64'))
im=Image.open(tempimg).convert("L")
im.thumbnail((28,28), Image.ANTIALIAS)
img_np= np.asarray(im)
img_np=img_np.flatten()
img_np.astype("float32")
img_np=img_np/255
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model.h5")
# evaluate loaded model on test data
loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
output=loaded_model.predict(img_np)
score=output.tolist()
return HttpResponse(score)
else:
return render(request, "digit/index.html")
我已经签出的链接是：
- Here
- Here
- and Here
修改
遵守同Rohan的建议，这是我的堆栈跟踪
Edit Complying with Rohan's suggestion, this is my stack trace
Internal Server Error: /home/
Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/django/core/handlers/base.py", line 149, in get_response
response = self.process_exception_by_middleware(e, request)
File "/usr/local/lib/python2.7/dist-packages/django/core/handlers/base.py", line 147, in get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
File "/home/vivek/keras/neural/digit/views.py", line 27, in home
output=loaded_model.predict(img_np)
File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 671, in predict
return self.model.predict(x, batch_size=batch_size, verbose=verbose)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1161, in predict
check_batch_dim=False)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 108, in standardize_input_data
str(array.shape))
Exception: Error when checking : expected dense_input_1 to have shape (None, 784) but got array with shape (784, 1)
此外，我有我以前用于训练网络的模型。
Also, I have my model that I used to train the network initially.
import numpy
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.utils import np_utils
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
(X_train, y_train), (X_test, y_test) = mnist.load_data()
for item in y_train.shape:
print item
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')
# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255
print X_train.shape
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
# define baseline model
def baseline_model():
# create model
model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, init='normal', activation='relu'))
model.add(Dense(num_classes, init='normal', activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
# build the model
model = baseline_model()
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), nb_epoch=20, batch_size=200, verbose=1)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Baseline Error: %.2f%%" % (100-scores[1]*100))
# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")
编辑
我尝试重塑img到（1,784），它也失败了，给出与这个问题的标题相同的错误
Edit I tried reshaping the img to (1,784) and it also failed, giving the same error as the title of this question
感谢您的帮助，并就如何添加问题发表评论。
Thanks for the help, and leave comments on how I should add to the question.
推荐答案
您正在要求神经网络评估784例病例，每次输入一次，而不是单次输入784例。我有同样的问题，我解决了它有一个数组与单个元素是一个数组的输入。看到下面的例子，第一个工作，而第二个给出你遇到的相同的错误。
You're asking the neural network to evaluate 784 cases with one input each instead of a single case with 784 inputs. I had the same problem and I solved it having an array with a single element which is an array of the inputs. See the example below, the first one works whereas the second one gives the same error you're experiencing.
model.predict(np.array([[0.5, 0.0, 0.1, 0.0, 0.0, 0.4, 0.0, 0.0, 0.1, 0.0, 0.0]]))
model.predict(np.array([0.5, 0.0, 0.1, 0.0, 0.0, 0.4, 0.0, 0.0, 0.1, 0.0, 0.0]))
希望这能解决它也适用于你：）
hope this solves it for you as well :)
这篇关于keras误差预测的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
