在神經網絡中獲取維度錯誤

Question

嗨，我試圖制作一個神經網絡並收到錯誤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

有人可以幫助理解它嗎？

Answer 1

在規范化之前/之后重塑數據

# ...
# 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