深度学习图片分类CNN模板

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毛利 发表于 2022/01/02 00:17:51 2022/01/02
【摘要】 构建一个字母ABC的手写识别网络, 要求给出算法误差收敛曲线，所给程序要有图片导入接口。 其中A，B，C都代表label，三个文件夹存在具体的图片。只要是这样类型的，直接套下面模板。 impo...

构建一个字母ABC的手写识别网络,

要求给出算法误差收敛曲线，所给程序要有图片导入接口。


其中A，B，C都代表label，三个文件夹存在具体的图片。只要是这样类型的，直接套下面模板。

import os
import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import tqdm

from tensorflow import keras
from keras import Input, Model, Sequential
from tensorflow.keras.regularizers import l2
from keras.layers import Dense, Flatten, InputLayer, Reshape, BatchNormalization, Dropout, Conv2D, MaxPooling2D
from tensorflow.keras.utils import plot_model

%matplotlib inline

  
 
    
  
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data_dir = './data'

  
 
    
  
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# Dict of labels
categories = {
'A': 0,
'B': 1,
'C': 2 
}

  
 
    
  
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def load_images(images_folder, img_size = (128,128), scale=False):

    # Store paths to images
    image_path = []
    for dirname, _, filenames in os.walk(images_folder):
        for filename in filenames:
            image_path.append(os.path.join(dirname, filename))

    print("There are {} images in {}".format(len(image_path), images_folder))
    
    # Load images and associated labels
    images = []
    labels = []

    for path in tqdm.tqdm(image_path):

        img = cv2.imread(path)    
        img = cv2.resize(img, img_size) # Resize the images

        img = np.array(img)

        images.append(img)
        labels.append(categories[path.split('/')[-2]]) # last folder before the image name is the category

    images = np.array(images)  
    images = images.astype(np.int64)
    
    if scale:
        images = images/255 # scale
        
    return image_path, images, np.asarray(labels)

  
 
    
  
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img_size = (128,128)
image_path, images, labels = load_images(data_dir, img_size=img_size)

# Resize
# images = np.array(images).reshape(-1,128,128,1)
images.shape

  
 
    
  
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There are 600 images in ./data


100%|██████████| 600/600 [00:03<00:00, 183.15it/s]





(600, 128, 128, 3)

  
 
    
  
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# 查看图片
plt.figure(figsize=(10,10))
random_inds = np.random.choice(len(image_path),36)
for i in range(36):
    plt.subplot(6,6,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    image_ind = random_inds[i]
    plt.imshow(np.squeeze(images[image_ind]), cmap=plt.cm.binary)
    
    label = list(categories.keys())[list(categories.values()).index(labels[image_ind])]
    plt.title(label)

  
 
    
  
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labels_df = pd.DataFrame(labels) 
labels_df.value_counts()

  
 
    
  
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2    201
0    201
1    198
dtype: int64

  
 
    
  
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dataset=[]
dataname=[]
count=0
for name in tqdm(os.listdir(data_dir)):
    path=os.path.join(data_dir,name)
    for im in os.listdir(path):
        image=cv2.imread(os.path.join(path,im))
        image2=np.resize(image,(50,50,3))
        dataset+=[image2]
        dataname+=[count]
    count=count+1

  
 
    
  
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100%|██████████| 3/3 [00:03<00:00,  1.06s/it]

  
 
    
  
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data=np.array(dataset)
dataname=np.array(dataname)

  
 
    
  
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data[0].shape


  
 
    
  
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(50, 50, 3)

  
 
    
  
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print(pd.Series(dataname).value_counts())


  
 
    
  
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1    202
2    201
0    198
dtype: int64

  
 
    
  
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len(categories)


  
 
    
  
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3

  
 
    
  
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from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, BatchNormalization

def build_cnn_model():
    cnn_model=tf.keras.Sequential([
      Conv2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=images.shape[1:]),
      MaxPooling2D(2,2),
      BatchNormalization(),
      Dropout(0.4),

      Conv2D(filters=64,kernel_size=(3,3),activation='relu', padding='same'),
      Conv2D(filters=64,kernel_size=(3,3),activation='relu', padding='same'),
      MaxPooling2D((2,2)),
      BatchNormalization(),
      Dropout(0.4),

      Conv2D(filters=128,kernel_size=(3,3),activation='relu', padding='same'),
      Conv2D(filters=128,kernel_size=(3,3),activation='relu', padding='same'),
      MaxPooling2D(2,2),
      BatchNormalization(),
      Dropout(0.4),
        
      Conv2D(filters=256,kernel_size=(3,3),activation='relu', padding='same'),
      Conv2D(filters=256,kernel_size=(3,3),activation='relu', padding='same'),
      MaxPooling2D(2,2),
      BatchNormalization(),
      Dropout(0.4),
        

      Conv2D(filters=128,kernel_size=(3,3),activation='relu', padding='same'),
      Conv2D(filters=128,kernel_size=(3,3),activation='relu', padding='same'),
      MaxPooling2D(2,2),
      BatchNormalization(),
      Dropout(0.4),
        
      Conv2D(filters=64,kernel_size=(3,3),activation='relu', padding='same'),
      Conv2D(filters=64,kernel_size=(3,3),activation='relu', padding='same'),
      MaxPooling2D((2,2)),
      BatchNormalization(),
      Dropout(0.4),

      Flatten(),

      Dense(units=len(categories),activation='softmax')
    ])

    return cnn_model
  
model = build_cnn_model()
# Initialize the model by passing some data through
model.predict(images[[0]])
# Print the summary of the layers in the model.
print(model.summary())

  
 
    
  
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Model: "sequential_3"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d_6 (Conv2D)           (None, 126, 126, 32)      896       
                                                                 
 max_pooling2d_6 (MaxPooling  (None, 63, 63, 32)       0         
 2D)                                                             
                                                                 
 batch_normalization (BatchN  (None, 63, 63, 32)       128       
 ormalization)                                                   
                                                                 
 dropout (Dropout)           (None, 63, 63, 32)        0         
                                                                 
 conv2d_7 (Conv2D)           (None, 63, 63, 64)        18496     
                                                                 
 conv2d_8 (Conv2D)           (None, 63, 63, 64)        36928     
                                                                 
 max_pooling2d_7 (MaxPooling  (None, 31, 31, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 31, 31, 64)       256       
 hNormalization)                                                 
                                                                 
 dropout_1 (Dropout)         (None, 31, 31, 64)        0         
                                                                 
 conv2d_9 (Conv2D)           (None, 31, 31, 128)       73856     
                                                                 
 conv2d_10 (Conv2D)          (None, 31, 31, 128)       147584    
                                                                 
 max_pooling2d_8 (MaxPooling  (None, 15, 15, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 15, 15, 128)      512       
 hNormalization)                                                 
                                                                 
 dropout_2 (Dropout)         (None, 15, 15, 128)       0         
                                                                 
 conv2d_11 (Conv2D)          (None, 15, 15, 256)       295168    
                                                                 
 conv2d_12 (Conv2D)          (None, 15, 15, 256)       590080    
                                                                 
 max_pooling2d_9 (MaxPooling  (None, 7, 7, 256)        0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 7, 7, 256)        1024      
 hNormalization)                                                 
                                                                 
 dropout_3 (Dropout)         (None, 7, 7, 256)         0         
                                                                 
 conv2d_13 (Conv2D)          (None, 7, 7, 128)         295040    
                                                                 
 conv2d_14 (Conv2D)          (None, 7, 7, 128)         147584    
                                                                 
 max_pooling2d_10 (MaxPoolin  (None, 3, 3, 128)        0         
 g2D)                                                            
                                                                 
 batch_normalization_4 (Batc  (None, 3, 3, 128)        512       
 hNormalization)                                                 
                                                                 
 dropout_4 (Dropout)         (None, 3, 3, 128)         0         
                                                                 
 conv2d_15 (Conv2D)          (None, 3, 3, 64)          73792     
                                                                 
 conv2d_16 (Conv2D)          (None, 3, 3, 64)          36928     
                                                                 
 max_pooling2d_11 (MaxPoolin  (None, 1, 1, 64)         0         
 g2D)                                                            
                                                                 
 batch_normalization_5 (Batc  (None, 1, 1, 64)         256       
 hNormalization)                                                 
                                                                 
 dropout_5 (Dropout)         (None, 1, 1, 64)          0         
                                                                 
 flatten_1 (Flatten)         (None, 64)                0         
                                                                 
 dense_6 (Dense)             (None, 3)                 195       
                                                                 
=================================================================
Total params: 1,719,235
Trainable params: 1,717,891
Non-trainable params: 1,344
_________________________________________________________________
None

  
 
    
  
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tf.keras.utils.plot_model(model, show_shapes=True)

  
 
    
  
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在这里插入图片描述

from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import shuffle
le = LabelEncoder()
labels = le.fit_transform(labels)
labels = to_categorical(labels)
labels[:10]

  
 
    
  
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array([[0., 1., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 1., 0.],
       [0., 1., 0.]], dtype=float32)

  
 
    
  
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model.compile(optimizer = "adam", loss = "binary_crossentropy", metrics = ["accuracy"])


  
 
    
  
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checkpoint_filepath = '/checkpoint.hdf5'
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_filepath,
    save_weights_only=True,
    monitor='val_accuracy',
    mode='max',
    save_best_only=True,
    save_freq=500)

  
 
    
  
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from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(
    monitor='val_accuracy', 
    patience=10, 
    min_delta=0.001, 
    mode='max',
    restore_best_weights=True
)

  
 
    
  
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datagen = ImageDataGenerator(horizontal_flip=True,vertical_flip=True,rotation_range=20,zoom_range=0.2,
                        width_shift_range=0.2,height_shift_range=0.2,shear_range=0.1,fill_mode="nearest")

  
 
    
  
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from tensorflow.keras.callbacks import ReduceLROnPlateau
reducelr = ReduceLROnPlateau(monitor = "val_accuracy",factor = 0.3, patience = 3,
                            min_delta = 0.001,mode = 'auto',verbose=1)

  
 
    
  
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from sklearn.model_selection import train_test_split
# Train, validation and test split

X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.10, random_state=7)

X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.20, random_state=1) 

  
 
    
  
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print("*-*-*-*-*-*")
print("Train")
print(X_train.shape)
print(y_train.shape)

print("*-*-*-*-*-*")
print("Validation")
print(X_val.shape)
print(y_val.shape)

print("*-*-*-*-*-*")
print("Test")
print(X_test.shape)
print(y_test.shape)

  
 
    
  
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*-*-*-*-*-*
Train
(432, 128, 128, 3)
(432, 3)
*-*-*-*-*-*
Validation
(108, 128, 128, 3)
(108, 3)
*-*-*-*-*-*
Test
(60, 128, 128, 3)
(60, 3)

  
 
    
  
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history = model.fit(X_train, y_train, 
                    batch_size = 32, 
                    epochs = 100, 
                    verbose = 1, 
                    validation_data = (X_val, y_val),
                    callbacks=[model_checkpoint_callback, early_stopping, reducelr])

  
 
    
  
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Epoch 1/1000
14/14 [==============================] - 8s 238ms/step - loss: 0.8036 - accuracy: 0.3588 - val_loss: 6.2921 - val_accuracy: 0.2963 - lr: 0.0010
Epoch 2/1000
14/14 [==============================] - 1s 95ms/step - loss: 0.8162 - accuracy: 0.3796 - val_loss: 5.2361 - val_accuracy: 0.2963 - lr: 0.0010
Epoch 3/1000
14/14 [==============================] - 1s 97ms/step - loss: 0.7190 - accuracy: 0.4537 - val_loss: 1.3893 - val_accuracy: 0.3333 - lr: 0.0010
Epoch 4/1000
14/14 [==============================] - 1s 97ms/step - loss: 0.6875 - accuracy: 0.4792 - val_loss: 0.7386 - val_accuracy: 0.3519 - lr: 0.0010
Epoch 5/1000
14/14 [==============================] - 1s 100ms/step - loss: 0.6144 - accuracy: 0.5949 - val_loss: 0.7014 - val_accuracy: 0.4259 - lr: 0.0010
Epoch 6/1000
14/14 [==============================] - 1s 97ms/step - loss: 0.5156 - accuracy: 0.7060 - val_loss: 0.7592 - val_accuracy: 0.4537 - lr: 0.0010
Epoch 7/1000
14/14 [==============================] - 1s 96ms/step - loss: 0.4904 - accuracy: 0.7384 - val_loss: 0.7034 - val_accuracy: 0.5370 - lr: 0.0010
Epoch 8/1000
14/14 [==============================] - 1s 97ms/step - loss: 0.3854 - accuracy: 0.7940 - val_loss: 0.6092 - val_accuracy: 0.5556 - lr: 0.0010
Epoch 9/1000
14/14 [==============================] - 1s 97ms/step - loss: 0.3313 - accuracy: 0.8241 - val_loss: 0.5192 - val_accuracy: 0.6389 - lr: 0.0010
Epoch 10/1000
14/14 [==============================] - 1s 93ms/step - loss: 0.2873 - accuracy: 0.8519 - val_loss: 0.5089 - val_accuracy: 0.6111 - lr: 0.0010
Epoch 11/1000
14/14 [==============================] - 1s 96ms/step - loss: 0.2346 - accuracy: 0.8981 - val_loss: 0.4359 - val_accuracy: 0.6852 - lr: 0.0010
Epoch 12/1000
14/14 [==============================] - 1s 94ms/step - loss: 0.2238 - accuracy: 0.8819 - val_loss: 0.4404 - val_accuracy: 0.6481 - lr: 0.0010
Epoch 13/1000
14/14 [==============================] - 1s 97ms/step - loss: 0.1954 - accuracy: 0.8912 - val_loss: 0.4215 - val_accuracy: 0.7500 - lr: 0.0010
Epoch 14/1000
14/14 [==============================] - 1s 100ms/step - loss: 0.1792 - accuracy: 0.9051 - val_loss: 0.1971 - val_accuracy: 0.9074 - lr: 0.0010
Epoch 15/1000
14/14 [==============================] - 1s 96ms/step - loss: 0.1608 - accuracy: 0.9144 - val_loss: 0.2836 - val_accuracy: 0.8056 - lr: 0.0010
Epoch 16/1000
14/14 [==============================] - 1s 95ms/step - loss: 0.1447 - accuracy: 0.9398 - val_loss: 0.2867 - val_accuracy: 0.7500 - lr: 0.0010
Epoch 17/1000
14/14 [==============================] - ETA: 0s - loss: 0.1215 - accuracy: 0.9375
Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.0003000000142492354.
14/14 [==============================] - 1s 95ms/step - loss: 0.1215 - accuracy: 0.9375 - val_loss: 0.1474 - val_accuracy: 0.9074 - lr: 0.0010
Epoch 18/1000
14/14 [==============================] - 1s 97ms/step - loss: 0.1023 - accuracy: 0.9537 - val_loss: 0.1186 - val_accuracy: 0.9352 - lr: 3.0000e-04
Epoch 19/1000
14/14 [==============================] - 1s 101ms/step - loss: 0.0992 - accuracy: 0.9606 - val_loss: 0.1074 - val_accuracy: 0.9444 - lr: 3.0000e-04
Epoch 20/1000
14/14 [==============================] - 1s 94ms/step - loss: 0.0837 - accuracy: 0.9676 - val_loss: 0.0917 - val_accuracy: 0.9444 - lr: 3.0000e-04
Epoch 21/1000
14/14 [==============================] - 1s 98ms/step - loss: 0.0788 - accuracy: 0.9699 - val_loss: 0.0877 - val_accuracy: 0.9444 - lr: 3.0000e-04
Epoch 22/1000
14/14 [==============================] - ETA: 0s - loss: 0.0809 - accuracy: 0.9722
Epoch 00022: ReduceLROnPlateau reducing learning rate to 9.000000427477062e-05.
14/14 [==============================] - 1s 95ms/step - loss: 0.0809 - accuracy: 0.9722 - val_loss: 0.0897 - val_accuracy: 0.9444 - lr: 3.0000e-04
Epoch 23/1000
14/14 [==============================] - 1s 95ms/step - loss: 0.0677 - accuracy: 0.9792 - val_loss: 0.0834 - val_accuracy: 0.9537 - lr: 9.0000e-05
Epoch 24/1000
14/14 [==============================] - 1s 93ms/step - loss: 0.0741 - accuracy: 0.9722 - val_loss: 0.0771 - val_accuracy: 0.9537 - lr: 9.0000e-05
Epoch 25/1000
14/14 [==============================] - 1s 94ms/step - loss: 0.0672 - accuracy: 0.9815 - val_loss: 0.0733 - val_accuracy: 0.9537 - lr: 9.0000e-05
Epoch 26/1000
14/14 [==============================] - ETA: 0s - loss: 0.0595 - accuracy: 0.9838
Epoch 00026: ReduceLROnPlateau reducing learning rate to 2.700000040931627e-05.
14/14 [==============================] - 1s 95ms/step - loss: 0.0595 - accuracy: 0.9838 - val_loss: 0.0694 - val_accuracy: 0.9537 - lr: 9.0000e-05
Epoch 27/1000
14/14 [==============================] - 1s 94ms/step - loss: 0.0631 - accuracy: 0.9838 - val_loss: 0.0699 - val_accuracy: 0.9537 - lr: 2.7000e-05
Epoch 28/1000
14/14 [==============================] - 1s 97ms/step - loss: 0.0591 - accuracy: 0.9861 - val_loss: 0.0705 - val_accuracy: 0.9537 - lr: 2.7000e-05
Epoch 29/1000
14/14 [==============================] - ETA: 0s - loss: 0.0635 - accuracy: 0.9838
Epoch 00029: ReduceLROnPlateau reducing learning rate to 8.100000013655517e-06.
14/14 [==============================] - 1s 95ms/step - loss: 0.0635 - accuracy: 0.9838 - val_loss: 0.0697 - val_accuracy: 0.9444 - lr: 2.7000e-05
Epoch 30/1000
14/14 [==============================] - 1s 95ms/step - loss: 0.0643 - accuracy: 0.9792 - val_loss: 0.0687 - val_accuracy: 0.9444 - lr: 8.1000e-06
Epoch 31/1000
14/14 [==============================] - 1s 100ms/step - loss: 0.0768 - accuracy: 0.9745 - val_loss: 0.0665 - val_accuracy: 0.9537 - lr: 8.1000e-06
Epoch 32/1000
14/14 [==============================] - ETA: 0s - loss: 0.0645 - accuracy: 0.9861
Epoch 00032: ReduceLROnPlateau reducing learning rate to 2.429999949526973e-06.
14/14 [==============================] - 1s 95ms/step - loss: 0.0645 - accuracy: 0.9861 - val_loss: 0.0656 - val_accuracy: 0.9537 - lr: 8.1000e-06
Epoch 33/1000
14/14 [==============================] - 1s 97ms/step - loss: 0.0635 - accuracy: 0.9792 - val_loss: 0.0645 - val_accuracy: 0.9630 - lr: 2.4300e-06
Epoch 34/1000
14/14 [==============================] - 1s 95ms/step - loss: 0.0606 - accuracy: 0.9838 - val_loss: 0.0636 - val_accuracy: 0.9630 - lr: 2.4300e-06
Epoch 35/1000
14/14 [==============================] - 1s 95ms/step - loss: 0.0620 - accuracy: 0.9907 - val_loss: 0.0628 - val_accuracy: 0.9630 - lr: 2.4300e-06
Epoch 36/1000
 9/14 [==================>...........] - ETA: 0s - loss: 0.0729 - accuracy: 0.9826WARNING:tensorflow:Can save best model only with val_accuracy available, skipping.
14/14 [==============================] - ETA: 0s - loss: 0.0682 - accuracy: 0.9861
Epoch 00036: ReduceLROnPlateau reducing learning rate to 7.289999985005124e-07.
14/14 [==============================] - 1s 95ms/step - loss: 0.0682 - accuracy: 0.9861 - val_loss: 0.0622 - val_accuracy: 0.9630 - lr: 2.4300e-06
Epoch 37/1000
14/14 [==============================] - 1s 96ms/step - loss: 0.0573 - accuracy: 0.9907 - val_loss: 0.0613 - val_accuracy: 0.9630 - lr: 7.2900e-07
Epoch 38/1000
14/14 [==============================] - 1s 97ms/step - loss: 0.0575 - accuracy: 0.9931 - val_loss: 0.0607 - val_accuracy: 0.9722 - lr: 7.2900e-07
Epoch 39/1000
14/14 [==============================] - 1s 94ms/step - loss: 0.0622 - accuracy: 0.9769 - val_loss: 0.0600 - val_accuracy: 0.9722 - lr: 7.2900e-07
Epoch 40/1000
14/14 [==============================] - 1s 96ms/step - loss: 0.0660 - accuracy: 0.9838 - val_loss: 0.0594 - val_accuracy: 0.9722 - lr: 7.2900e-07
Epoch 41/1000
14/14 [==============================] - ETA: 0s - loss: 0.0614 - accuracy: 0.9884
Epoch 00041: ReduceLROnPlateau reducing learning rate to 2.1870000637136398e-07.
14/14 [==============================] - 1s 95ms/step - loss: 0.0614 - accuracy: 0.9884 - val_loss: 0.0591 - val_accuracy: 0.9722 - lr: 7.2900e-07
Epoch 42/1000
14/14 [==============================] - 1s 94ms/step - loss: 0.0605 - accuracy: 0.9792 - val_loss: 0.0583 - val_accuracy: 0.9722 - lr: 2.1870e-07
Epoch 43/1000
14/14 [==============================] - 1s 99ms/step - loss: 0.0529 - accuracy: 0.9954 - val_loss: 0.0582 - val_accuracy: 0.9722 - lr: 2.1870e-07
Epoch 44/1000
14/14 [==============================] - ETA: 0s - loss: 0.0500 - accuracy: 0.9884
Epoch 00044: ReduceLROnPlateau reducing learning rate to 6.561000276406048e-08.
14/14 [==============================] - 1s 95ms/step - loss: 0.0500 - accuracy: 0.9884 - val_loss: 0.0580 - val_accuracy: 0.9722 - lr: 2.1870e-07
Epoch 45/1000
14/14 [==============================] - 1s 94ms/step - loss: 0.0613 - accuracy: 0.9861 - val_loss: 0.0581 - val_accuracy: 0.9722 - lr: 6.5610e-08
Epoch 46/1000
14/14 [==============================] - 1s 94ms/step - loss: 0.0672 - accuracy: 0.9861 - val_loss: 0.0572 - val_accuracy: 0.9722 - lr: 6.5610e-08
Epoch 47/1000
14/14 [==============================] - ETA: 0s - loss: 0.0511 - accuracy: 0.9931
Epoch 00047: ReduceLROnPlateau reducing learning rate to 1.9683000829218145e-08.
14/14 [==============================] - 1s 96ms/step - loss: 0.0511 - accuracy: 0.9931 - val_loss: 0.0574 - val_accuracy: 0.9722 - lr: 6.5610e-08
Epoch 48/1000
14/14 [==============================] - 1s 99ms/step - loss: 0.0622 - accuracy: 0.9861 - val_loss: 0.0570 - val_accuracy: 0.9722 - lr: 1.9683e-08

  
 
    
  
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plt.plot(history.history["accuracy"])
plt.plot(history.history["val_accuracy"])
plt.title("Model accuracy")
plt.ylabel("Accuracy")
plt.xlabel("Epoch")
plt.legend(["Train", "Test"], loc = "upper left")
plt.show()

  
 
    
  
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请添加图片描述

plt.plot(history.history["loss"])
plt.plot(history.history["val_loss"])
plt.title("Model loss")
plt.ylabel("Loss")
plt.xlabel("Epoch")
plt.legend(["Train", "Test"], loc = "upper left")
plt.show()

  
 
    
  
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请添加图片描述

def predict_class(img):
    # Resize
    img = img.reshape(1,128,128,3)
    # Predict
    predictions = model.predict(img)
    true_prediction = [tf.argmax(pred) for pred in predictions]
    true_prediction = np.array(true_prediction)
    
    # Return label corresponding to predicted index
    return list(categories.keys())[list(categories.values()).index(true_prediction)]
    

  
 
    
  
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# Predict on test set
y_pred = model.predict(X_test)

  
 
    
  
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# From categorical outputs to discrete values
y_pred_ = [np.argmax(y) for y in y_pred]
y_test_ = [np.argmax(y) for y in y_test]

  
 
    
  
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from sklearn.metrics import classification_report
print(classification_report(y_test_, y_pred_))

  
 
    
  
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              precision    recall  f1-score   support

           0       1.00      0.96      0.98        25
           1       0.85      1.00      0.92        11
           2       1.00      0.96      0.98        24

    accuracy                           0.97        60
   macro avg       0.95      0.97      0.96        60
weighted avg       0.97      0.97      0.97        60

  
 
    
  
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plt.figure(figsize=(10,10))
random_inds = np.random.choice(X_test.shape[0],36)
for i in range(36):
    plt.subplot(6,6,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    image_ind = random_inds[i]
    plt.imshow(np.squeeze(X_test[image_ind]), cmap=plt.cm.binary)
    
    # Predict and get label
    label = predict_class(X_test[image_ind])
    plt.xlabel(label)

  
 
    
  
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model.save("model.h5")


  
 
    
  
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# 识别接口
def predict(path,model_str,img_size = (128,128)):
  new_model = tf.keras.models.load_model(model_str)
  img = cv2.imread(path)
  img = cv2.resize(img, img_size) # Resize the images
  img = np.array(img)    
  # Resize
  img = img.reshape(1,128,128,3)
  # Predict
  predictions = new_model.predict(img)
  true_prediction = [tf.argmax(pred) for pred in predictions]
  true_prediction = np.array(true_prediction)
  
  # Return label corresponding to predicted index
  return list(categories.keys())[list(categories.values()).index(true_prediction)]
    
predict("./data/A/051.jpg","model.h5")

  
 
    
  
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'A'

  
 
    
  
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predict("./data/B/048.jpg","model.h5")

  
 
    
  
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'B'

  
 
    
  
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predict("./data/C/050.jpg","model.h5")

  
 
    
  
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'C'

  
 
    
  
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代码资料下载：　https://download.csdn.net/download/weixin_44510615/72775830

文章来源: maoli.blog.csdn.net，作者：刘润森！，版权归原作者所有，如需转载，请联系作者。

原文链接：maoli.blog.csdn.net/article/details/122266896

【版权声明】本文为华为云社区用户转载文章，如果您发现本社区中有涉嫌抄袭的内容，欢迎发送邮件进行举报，并提供相关证据，一经查实，本社区将立刻删除涉嫌侵权内容，举报邮箱： cloudbbs@huaweicloud.com
深度学习 神经网络
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