Question

尝试将以下代码应用于MNIST样本数据集进行培训和测试时出错。请帮助

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：期望dense_input_6有形状（无，784）但是有形状的数组（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

错误：检查模型输入时出错：期望dense_input_6具有形状（无，784）但是具有形状的数组（784L，1L）

1 个答案: