Question

我想用Keras进行图像识别，但不断出现如下错误：

ValueError：检查输入时出错：期望input_9有4个维度，但得到的数组有形状（100,300,300）

我尝试更改与尺寸相关的参数的值，也试图重塑图像，但仍然出错。 事实上，我不明白为什么会收到此错误。为什么它需要4个维度？

这是我的代码：

import os
import numpy as np
import pandas as pd
import scipy
import sklearn
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Convolution2D, Flatten, MaxPooling2D, Reshape, InputLayer
import cv2
from skimage import io

import urllib2
from PIL import Image
import numpy as np
%matplotlib inline

我从imagenet中选择了50张玫瑰图片和50张向日葵图片：

rose_file = "http://www.image-net.org/api/text/imagenet.synset.geturls?wnid=n04971313"
sunflower_file = "http://www.image-net.org/api/text/imagenet.synset.geturls?wnid=n11978713"

images = []
image_num = 50

rose_urls = urllib2.urlopen(rose_file)
rose_ct = 0
for rose_url in rose_urls: 
    try:
        resp = urllib2.urlopen(rose_url)
        rose_image = np.asarray(bytearray(resp.read()), dtype="uint8")
        images.append(rose_image)
        rose_ct += 1
        if rose_ct == image_num:  # only use 50 images here, otherwise, loading time is too long
            break
    except:   # some images are no longer available
        pass

sunflower_urls = urllib2.urlopen(sunflower_file)
sunflower_ct = 0
for sunflower_url in sunflower_urls: 
    try:
        resp = urllib2.urlopen(sunflower_url)
        sunflower_image = np.asarray(bytearray(resp.read()), dtype="uint8")
        images.append(sunflower_image)
        sunflower_ct += 1
        if sunflower_ct == image_num:   # only use 50 images here, otherwise, loading time is too long
            break
    except:   # some images are no longer available
        pass

将训练图像调整为300 * 300：

from keras.utils.np_utils import to_categorical

for i in range(len(images)):
    images[i]=cv2.resize(np.array(images[i]),(300,300))

images = np.array(images)

labels = [0 for i in range(image_num)]
labels.extend([1 for j in range(image_num)])
labels = np.array(labels)
labels = to_categorical(labels)

构建模型：

filters=10
filtersize=(5,5)

epochs=7
batchsize=128

input_shape=(300,300, 3)

model = Sequential()
model.add(keras.layers.InputLayer(input_shape=input_shape))
model.add(keras.layers.convolutional.Conv2D(filters, filtersize, strides=(1, 1), 
                                        padding='valid', data_format="channels_last", activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(keras.layers.Flatten())

model.add(keras.layers.Dense(units=2, input_dim=10, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(images, labels, epochs=epochs, batch_size=batchsize, validation_split=0.3)

model.summary()

在这里，我尝试将input_shape=(300,300, 3)更改为input_shape=(300,300, 3, 0)，希望这意味着4个维度，但却出错：

输入0与图层conv2d_13不兼容：预期ndim = 4，发现ndim = 5

你知道为什么我会收到这些错误吗？以及如何处理这个问题？

Python Keras - 如何生成正确的图像维度

0 个答案: