错误：检查模型输入时出错：预期dense_input_6 具有形状（无，784）但得到的数组具有形状（784L，1L）

Question

尝试将以下代码应用于 MNIST 示例数据集以进行训练和测试时出现错误。请帮忙

以下是我的代码：

import pandas
import numpy
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)
# Read in the TRAINING dataset 
f = open("C:/Users/USER/Desktop/mnist/mnist_train_100.csv", 'r')
a = f.readlines() # place everythig in a lsit called 'a'
#print(a)
f.close()
# go through the list a and split by comma 
output_nodes = 10
for record in a: #go through the big list "a"
    all_values = record.split(',')
    X_train = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01   
    y_train = numpy.zeros(output_nodes) + 0.01
    y_train[int(all_values[0])] = 0.99
# Read in the TEST data set and then split
f = open("C:/Users/USER/Desktop/mnist/mnist_test_10.csv", 'r')
a = f.readlines() # place everythig in a lsit called 'a'
#print(a)
f.close()
# go through the list a and split by comma 
for record in a: #go through the big list "a"
    all_values = record.split(',')
    X_test = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01    
    y_test = numpy.zeros(output_nodes) + 0.01
    y_test[int(all_values[0])] = 0.99

num_pixels = len(X_train)
# 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(output_nodes, 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=10, batch_size=200,verbose=2)

我收到以下错误：

异常：检查模型输入时出错：预期dense_input_6 具有形状（无，784），但得到的数组具有形状（784L，1L）

Answer 1

我假设您正在使用this tutorial。

我建议使用 pandas 来阅读您的格式：

import pandas as pd
import numpy as np

data = pd.read_csv('mnist_train_100.csv', header=None)

# numpy array of shape (100, 784), type float32
X_train = data.ix[:, 1:].values.astype(np.float32) 

# numpy array of shape (100,), type int64
y_train = data.ix[:, 0].values