Error: Error when checking model input: expected dense_input_6 to have shape (None, 784) but got array with shape (784L, 1L)

Question

I get an error when trying to apply the below code onto the MNIST sample dataset for both training and testing. Please helpe

The following is my code:

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)

I get the following error:

Exception: Error when checking model input: expected dense_input_6 to have shape (None, 784) but got array with shape (784L, 1L)

I think he uses [this tutorial](http://makeyourownneuralnetwork.blogspot.fr/2015/03/the-mnist-dataset-of-handwitten-digits.html), which gives MNIST as a CSV file. — Olivier Moindrot, Jun 22 '16 at 08:18

Olivier Moindrot · Answer 1 · 2016-06-22T08:18:39.123

0

I assume you are working with this tutorial.

I would recommend using pandas to read your format:

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

edited Jun 22 '16 at 08:18

answered Jun 18 '16 at 22:26

Olivier Moindrot

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This is a nice alternative, but doesn't answer the OP's question. – Monica Heddneck Dec 28 '16 at 03:02

Error: Error when checking model input: expected dense_input_6 to have shape (None, 784) but got array with shape (784L, 1L)

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