[嗨,我试图建立一个神经网络并出现错误ValueError: Error when checking input: expected conv2d_27_input to have 4 dimensions, but got array with shape (60000, 28, 28)
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.constraints import maxnorm
from keras.optimizers import SGD
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
# load data
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# normalize inputs from 0-255 to 0.0-1.0
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train = x_train / 255.0
x_test = x_test / 255.0
# Encode the outputs
y_train = np_utils.to_categorical(y_train) #Converts a class vector (integers) to binary class matrix.
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
# Build the model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(28, 28, 2), activation='relu'))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
epochs = 5
lrate = 0.002
decay = lrate/epochs
有人可以帮助您理解吗?
# ...
# reshape to be [samples][width][height][channels]
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32')
X_train = X_train / 255.0
X_test = X_test / 255.0