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python - 将图像输入已经训练好的 TensorFlow CNN

问题描述

我对 Python 有点陌生，对 TensorFlow 也很陌生。我已经学习了几个教程，并且通过这个视频被困在这个colab上。训练运行完美，我有一个保存的模型。现在我想加载该模型并将我自己的一张图像输入其中。这是我的尝试：

import tensorflow as tf
import cv2
from tensorflow import keras
model = tf.keras.models.load_model('rps.h5')
model.summary()

img = cv2.imread('my_hand_paper.png')
print(model.predict_classes(img))

但我收到以下错误：

ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [None, 300, 3]

我的图像是 300x300，就像训练图像一样。我想问题是我必须以与训练数据类似的方式准备图像，但我不确定如何。这是准备训练数据的方式：

training_datagen = ImageDataGenerator(
  rescale = 1./255,
  rotation_range=40,
  width_shift_range=0.2,
  height_shift_range=0.2,
  shear_range=0.2,
  zoom_range=0.2,
  horizontal_flip=True,
  fill_mode='nearest')

train_generator = training_datagen.flow_from_directory(
  TRAINING_DIR,
  target_size=(150,150),
  class_mode='categorical',
  batch_size=126)

摘要（）的输出：

 Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 148, 148, 64)      1792      
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 74, 74, 64)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 72, 72, 64)        36928     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 36, 36, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 34, 34, 128)       73856     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 17, 17, 128)       0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 15, 15, 128)       147584    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128)         0         
_________________________________________________________________
flatten (Flatten)            (None, 6272)              0         
_________________________________________________________________
dropout (Dropout)            (None, 6272)              0         
_________________________________________________________________
dense (Dense)                (None, 512)               3211776   
_________________________________________________________________
dense_1 (Dense)              (None, 3)                 1539      
=================================================================
Total params: 3,473,475
Trainable params: 3,473,475
Non-trainable params: 0
_________________________________________________________________

标签： pythontensorflowconv-neural-network

解决方案

当您想在推理中使用您的网络时，您仍然必须使用批量大小。在您的情况下，批量大小为 1。

您可以使用以下代码添加批处理：

img = cv2.resize(img, (150,150))    
img = tf.expand_dims(img , 0)

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