keras 预测错误 [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.但是每当我运行它时，我都会收到内部服务器错误和服务器端代码错误.错误说异常:检查时出错:预期dense_input_1具有形状(无，784)但得到形状为(784，1)的数组.我唯一的主要观点是

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")

我检查过的链接是:

• 这里
• 这里
• 和这里

编辑遵照 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)

另外，我有我最初用来训练网络的模型.

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")

编辑我尝试将 img 重新整形为 (1,784) 但它也失败了，给出与此问题标题相同的错误

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，并就我应该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

您要求神经网络评估 784 个案例，每个案例有一个输入，而不是单个案例，每个案例有 784 个输入.我遇到了同样的问题，我解决了它，它有一个包含单个元素的数组，该数组是一个输入数组.请参见下面的示例，第一个有效，而第二个给出了您遇到的相同错误.

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]))

希望这也能为您解决问题:)

hope this solves it for you as well :)

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