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运行了Web界面。但是，每当我运行它时，都会收到内部服务器错误和服务器端代码错误。该错误显示
异常：检查时出错：预期density_input_1具有形状（None，784）但具有形状为（784，1）的数组 。我唯一的主要观点是

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")

我签出的链接是：

• 这里
• 和这里

编辑 符合Rohan的建议，这是我的堆栈跟踪

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)

另外，我有最初用于训练网络的模型。

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 我尝试将img重塑为（1,784），它也失败了，给出了与该问题的标题相同的错误

感谢您的帮助，并就如何添加到问题发表评论。