Question

我正在尝试将model.predict_classes(x)用于训练有素的模型，但出现错误：

ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (8, 4)

我用相同的错误信息检查了其他问题，但没有一个解决我的问题，或者至少我无法理解特别解决我的问题意味着什么。

为了能够重现该错误，您可以在Google colab中运行以下代码段：

首次加载数据集：

from google_drive_downloader import GoogleDriveDownloader as gdd

gdd.download_file_from_google_drive(
    file_id='13WSlx4cmXh3wfvzNEXbZAbc2a1RHpPPP',
    dest_path='./data/klasifikacia.zip', 
    unzip=True)

然后进行负荷训练模型：

gdd.download_file_from_google_drive(
    file_id='1k3Lz79cF40peKCf-UdobyIUdbKZ2Onfp',
    dest_path='./data/model.h5', 
    unzip=False)

最后，运行预测：

import keras
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import load_model
from matplotlib import pyplot as plt
import matplotlib.pyplot as plt
%matplotlib inline

test_path = './data/test'
model_name = 'keras_drone_trained_model.h5'

test_batches = ImageDataGenerator().flow_from_directory(test_path, target_size=(100, 100), classes=['biker', 'pedestrian', 'golf_cart', 'skater'], batch_size=8)
test_datagen = ImageDataGenerator(rescale=1./255)

def plots(ims, figsize=(12,6), rows=1, interp=False, titles=None):
  if type(ims[0]) is np.ndarray:
    ims = np.array(ims).astype(np.uint8)
    if(ims.shape[-1] != 3):
      ims = ims.transpose((0,2,3,1))
    f = plt.figure(figsize=figsize)
    cols = len(ims)//rows if len(ims)%2 == 0 else len(ims)//rows + 1
    for i in range(len (ims)):
      sp = f.add_subplot(rows, cols, i+1)
      sp.axis('Off')
      if titles is not None:
        sp.set_title(titles[i], fontsize=14)
        plt.imshow(ims[i], interpolation=None if interp else 'none')

test_imgs, test_labels = next(test_batches)
plots(test_imgs, titles=test_labels)

classes = test_batches.class_indices
print(classes)

model = load_model('data/model.h5')

x = model.predict_generator(test_batches, steps=1, verbose=0)
predict = model.predict_classes(x)
predict

所以输入应该有4个维，但是什么数组的形状为（8，4）？我的代码中的问题出在哪里？如果您还需要查看我的模型，请在评论中告诉我。

编辑：

summary()的几行：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_7 (Conv2D)            (None, 100, 100, 32)      896       
_________________________________________________________________
activation_7 (Activation)    (None, 100, 100, 32)      0         
_________________________________________________________________
batch_normalization_7 (Batch (None, 100, 100, 32)      128       
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 100, 100, 32)      9248

预期conv2d_1_input具有4个维度，但数组的形状为（8，4）

0 个答案: