python - 4维数据经过卷积神经网络后变成2维

问题描述

我正在训练一个具有两种输入类型的神经元网络：图像和 BR（红色上的蓝色，它是一种非图像特征，如身高、体重......）。为此，我在 keras 中使用 fit 函数，并将图像转换为列表以供输入。但我不知道为什么图像列表，它有 4 个维度的形状在适应时变成了 2 个维度，我得到了如下错误：

检查输入时出错：预期的 dense_1_input 具有 3 个维度，但得到的数组具有形状 (1630, 1)

当我将图像列表转换为数组时，我检查了 image_array 的形状，它正好有 4 个维度（特别是它的形状是 1630、60、60、3）。即使在 fit 函数之前，它仍然具有相同的形状。所以我真的不知道为什么形状变成了（1630,1）。谁能为我解释一下？

这是我的代码：

from keras.utils.np_utils import to_categorical
import pandas as pd 
import numpy as np
import os
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.layers import Input, Activation, Dropout, Flatten, Dense,Concatenate, concatenate,Reshape, BatchNormalization, Merge
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.optimizers import Adagrad
from sklearn import preprocessing
from scipy.misc import imread
import time
from PIL import Image 
import cv2
img_width, img_height = 60, 60
img_list = []
BR_list = []
label_list = []
data_num = 1630

folder1 = "cut2/train/sugi/"

folder2 = "cut2/train/hinoki/"

def imgConvert(file_path):
    img = imread(file_path,flatten = True)
    img = np.arange(1*3*60*60).reshape((60,60,3))
    img = np.array(img).reshape(60,60,3)
    img = img.astype("float32")
    return img

def B_and_R(img_path):
    img = cv2.imread(img_path)
    B = 0
    R = 0

    for i in range(25,35):
    #print(i)
        for j in range(25,35):
            B = B+img[i,j,0]
            R = R+img[i,j,2]
        #(j)
        #(img[i,j])

    ave_B = B/100
    ave_R = R/100

    BR = ave_B/ave_R
    return BR

def getData(path,pollen):
    for the_file in os.listdir(path):
        #print(the_file)
        file_path = os.path.join(path, the_file)
        B_over_R = B_and_R(file_path)
        img_arr = imgConvert(file_path)
        #writer.writerow([img_arr,B_over_R,"sugi"])
        img_list.append(img_arr)
        BR_list.append(B_over_R)
        lb = np.zeros(2)
        if pollen == "sugi":
            lb[0] +=1
        else:
            lb[1] +=1
        label_list.append(lb)

if __name__ == '__main__':

    getData(folder1,"sugi")
    getData(folder2,"hinoki")

    img_arr = np.array(img_list)
    print(img_arr.shape)
    #.reshape(img_list[0],1,img_width,img_height)
    img_arr.astype("float32")
    img_arr /= 255
    print(img_arr.shape)
    img_array = np.expand_dims(img_arr, axis = 0)
    img_array = img_array[0,:,:,:,:]
    print(img_array.shape)

    """
    datagen = ImageDataGenerator(
        featurewise_center=True,
        featurewise_std_normalization=True,
        rotation_range=20,
        width_shift_range=0.2,
        height_shift_range=0.2,
        horizontal_flip=True)
    datagen.fit(img_array)
    """
    #img_array = img_array.reshape(img_array[0],1,img_width,img_height)

    print(img_array.shape)
    label_arr = np.array(label_list)
    print(label_arr.shape)
    #label_array = np.expand_dims(label_arr, axis = 0)
    #label_array = label_array[0,:,:,:,:]

    BR_arr = np.array(BR_list)
    print(BR_arr.shape)
    #BR_array = np.expand_dims(BR_arr, axis = 0)
    #BR_array = BR_array[0,:,:,:,:]

    #print(len([img_arr,BR_arr]))

    input_tensor = Input(shape=(img_width, img_height,3))
    vgg16 = VGG16(include_top=False, weights='imagenet', input_tensor=input_tensor)

    # FC層の作成
    top_model = Sequential()
    top_model.add(Flatten(input_shape=vgg16.output_shape[1:]))

#print(top_model.summary())

    # VGG16とFC層を結合してモデルを作成
    branch1 = Model(input=vgg16.input, output=top_model(vgg16.output))
#model.summary()
    print(branch1.summary())


    branch2 = Sequential()
    branch2.add(Dense(1, input_shape=(data_num,1), activation='sigmoid'))
    #branch1.add(Reshape(BR.shape, input_shape = BR.shape))
    branch2.add(BatchNormalization())
    branch2.add(Flatten())

    print(branch2.summary())
    merged = Merge([branch1, branch2], mode = "concat")

    model = Sequential()
    model.add(merged)
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(2, activation='softmax'))

   #last_model = Model(input = [branch1.input,branch2.input],output=model())
    print(model.summary())
    model.compile(loss='categorical_crossentropy',
              optimizer=optimizers.SGD(lr=1e-3, momentum=0.9),
              metrics=['accuracy'])
    print(img_array.shape)
    model.fit([img_array,BR_arr], label_arr,
            epochs=5, batch_size=100, verbose=1)

标签： pythonkeras

python - 4维数据经过卷积神经网络后变成2维

问题描述

解决方案

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