Question

我和DL一起学习DL。遵循MNIST教程，但在调用model.fit时收到以下错误。代码：

import keras
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Activation, Dropout, Dense, Flatten, Convolution2D, MaxPool2D, MaxPooling2D

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(x_train.shape[0], 1, 28, 28)
x_test = x_test.reshape(x_test.shape[0], 1, 28, 28)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

Y_train = keras.utils.np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)

model = Sequential()

model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(1,28,28)))
model.add(Convolution2D(32, 3, 3, activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

model.compile (  loss='categorical_crossentropy',
                optimizer='adam',
                metrics=['accuracy'])

model.fit(x_train, Y_train, batch_size=32, nb_epoch=10, verbose=1)


score = model.evaluate(x_test, Y_test, verbose=0)

错误：

--------------------------------------------------------------------------- ValueError                                Traceback (most recent call last) <ipython-input-41-d2e69a06c966> in <module>()
      2                  optimizer='adam',
      3                 metrics=['accuracy'])
----> 4 model.fit(x_train, Y_train, batch_size=32, epochs=10)

/anaconda/lib/python3.6/site-packages/keras/models.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch,
**kwargs)
    861                               class_weight=class_weight,
    862                               sample_weight=sample_weight,
--> 863                               initial_epoch=initial_epoch)
    864 
    865     def evaluate(self, x, y, batch_size=32, verbose=1,

/anaconda/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)    1356             class_weight=class_weight,    1357             check_batch_axis=False,
-> 1358             batch_size=batch_size)    1359         # Prepare validation data.    1360         if validation_data:

/anaconda/lib/python3.6/site-packages/keras/engine/training.py in
_standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)    1232                                  self._feed_input_shapes,    1233                                     check_batch_axis=False,
-> 1234                                     exception_prefix='input')    1235         y = _standardize_input_data(y, self._feed_output_names,   1236                                     output_shapes,

/anaconda/lib/python3.6/site-packages/keras/engine/training.py in
_standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    138                             ' to have shape ' + str(shapes[i]) +
    139                             ' but got array with shape ' +
--> 140                             str(array.shape))
    141     return arrays
    142 

ValueError: Error when checking input: expected conv2d_11_input to have shape (None, 28, 28, 1) but got array with shape (60000, 1, 28, 28)

我做错了什么？

Answer 1

更改此行：

x_train = x_train.reshape(x_train.shape[0], 1, 28, 28)

到

x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)

顺便提一句x_test。

卷积神经网络中的Keras形状误差

1 个答案: