keras 入门建模型案例

墨蓝 2021-06-24 16:00 568阅读 0赞

[原链接][Link 1]

In \[1\]:

import os
    import cv2
    import random
    import numpy as np
    import pandas as pd
    import seaborn as sns
    import matplotlib.pyplot as plt
    
    from tqdm import tqdm
    from sklearn.utils import shuffle
    from sklearn.model_selection import train_test_split
    
    %matplotlib inline
    %config InlineBackend.figure_format = 'retina'

### 导入数据 ###

In \[2\]:

df_train = pd.read_csv('train.csv', header=None)
    df_train.columns = ['image_id', 'class', 'label']
    df_train.head()

Out\[2\]:

<table> 
 <thead> 
  <tr> 
   <th>&nbsp;</th> 
   <th>image_id</th> 
   <th>class</th> 
   <th>label</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>Images/collar_design_labels/4d8a38b29930a403e5...</td> 
   <td>collar_design_labels</td> 
   <td>nnynn</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>Images/collar_design_labels/bd0981f231180d2b00...</td> 
   <td>collar_design_labels</td> 
   <td>nynnn</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>Images/collar_design_labels/26937e1724feadfe39...</td> 
   <td>collar_design_labels</td> 
   <td>ynnnn</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>Images/collar_design_labels/cf4140ec542887270f...</td> 
   <td>collar_design_labels</td> 
   <td>nynnn</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>Images/collar_design_labels/50644b2b9de045f2d1...</td> 
   <td>collar_design_labels</td> 
   <td>nynnn</td> 
  </tr> 
 </tbody> 
</table>

In \[3\]:

classes = ['collar_design_labels', 'neckline_design_labels', 'skirt_length_labels', 
               'sleeve_length_labels', 'neck_design_labels', 'coat_length_labels', 'lapel_design_labels', 
               'pant_length_labels']
    len(classes)

Out\[3\]:

### 加载数据 ###

In \[4\]:

cur_class = classes[0]
    df_load = df_train[(df_train['class'] == cur_class)].copy()
    df_load.reset_index(inplace=True)
    del df_load['index']
    
    print('{0}: {1}'.format(cur_class, len(df_load)))
    df_load.head()

collar_design_labels: 8393

Out\[4\]:

In \[5\]:

df_load[(df_load.index == 2)]

Out\[5\]:

<table> 
 <thead> 
  <tr> 
   <th>&nbsp;</th> 
   <th>image_id</th> 
   <th>class</th> 
   <th>label</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>2</th> 
   <td>Images/collar_design_labels/26937e1724feadfe39...</td> 
   <td>collar_design_labels</td> 
   <td>ynnnn</td> 
  </tr> 
 </tbody> 
</table>

In \[6\]:

n = len(df_load)
    n_class = len(df_load['label'][0])
    width = 299
    
    X = np.zeros((n, width, width, 3), dtype=np.uint8)
    y = np.zeros((n, n_class), dtype=np.uint8)
    
    for i in tqdm(range(n)):
        tmp_label = df_load['label'][i]
        if len(tmp_label) > n_class:
            print(df_load['image_id'][i])
        X[i] = cv2.resize(cv2.imread('./train/{0}'.format(df_load['image_id'][i])), (width, width))
        y[i][tmp_label.find('y')] = 1

100%|██████████| 8393/8393 [00:31<00:00, 269.94it/s]

In \[7\]:

plt.figure(figsize=(12, 7))
    for i in range(8):
        random_index = random.randint(0, n-1)
        plt.subplot(2, 4, i+1)
        plt.imshow(X[random_index][:,:,::-1])
        plt.title(y[random_index])

### 提取特征 ###

In \[8\]:

from keras.layers import *
    from keras.models import *
    from keras.callbacks import *
    from keras.optimizers import *
    from keras.applications import *
    from keras.regularizers import *
    from keras.applications.inception_v3 import preprocess_input

/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
      from ._conv import register_converters as _register_converters
    Using TensorFlow backend.

In \[9\]:

cnn_model = InceptionResNetV2(include_top=False, input_shape=(width, width, 3), weights='imagenet')
    inputs = Input((width, width, 3))
    
    x = inputs
    x = Lambda(preprocess_input, name='preprocessing')(x)
    x = cnn_model(x)
    x = GlobalAveragePooling2D()(x)
    x = Dropout(0.5)(x)
    x = Dense(n_class, activation='softmax', name='softmax')(x)
    
    model = Model(inputs, x)

### 划分训练、测试集 ###

In \[10\]:

X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.12, random_state=42)
    X_train.shape, y_train.shape

Out\[10\]:

((7385, 299, 299, 3), (7385, 5))

In \[11\]:

adam = Adam(lr=0.001)
    prefix_cls = cur_class.split('_')[0]
    
    model.compile(optimizer=adam,
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
    
    checkpointer = ModelCheckpoint(filepath='./models/{0}.best.h5'.format(prefix_cls), verbose=1, 
                                   save_best_only=True)
    
    h = model.fit(X_train, y_train, batch_size=32, epochs=80, 
                  callbacks=[EarlyStopping(patience=3), checkpointer], 
                  shuffle=True, 
                  validation_split=0.1)

Train on 6646 samples, validate on 739 samples
    Epoch 1/80
    6624/6646 [============================>.] - ETA: 0s - loss: 0.9380 - acc: 0.6532Epoch 00001: val_loss improved from inf to 1.10376, saving model to ./models/collar.best.h5
    6646/6646 [==============================] - 196s 29ms/step - loss: 0.9367 - acc: 0.6539 - val_loss: 1.1038 - val_acc: 0.6766
    Epoch 2/80
    6624/6646 [============================>.] - ETA: 0s - loss: 0.6084 - acc: 0.7876Epoch 00002: val_loss improved from 1.10376 to 0.75603, saving model to ./models/collar.best.h5
    6646/6646 [==============================] - 183s 27ms/step - loss: 0.6077 - acc: 0.7880 - val_loss: 0.7560 - val_acc: 0.7361
    Epoch 3/80
    6624/6646 [============================>.] - ETA: 0s - loss: 0.4251 - acc: 0.8552Epoch 00003: val_loss did not improve
    6646/6646 [==============================] - 181s 27ms/step - loss: 0.4264 - acc: 0.8554 - val_loss: 1.5847 - val_acc: 0.5372
    Epoch 4/80
    6624/6646 [============================>.] - ETA: 0s - loss: 0.3442 - acc: 0.8819Epoch 00004: val_loss did not improve
    6646/6646 [==============================] - 181s 27ms/step - loss: 0.3444 - acc: 0.8816 - val_loss: 0.8974 - val_acc: 0.7294
    Epoch 5/80
    6624/6646 [============================>.] - ETA: 0s - loss: 0.2424 - acc: 0.9179Epoch 00005: val_loss did not improve
    6646/6646 [==============================] - 181s 27ms/step - loss: 0.2420 - acc: 0.9180 - val_loss: 1.4803 - val_acc: 0.6455

In \[12\]:

plt.figure(figsize=(10, 4))
    plt.subplot(1, 2, 1)
    plt.plot(h.history['loss'])
    plt.plot(h.history['val_loss'])
    plt.legend(['loss', 'val_loss'])
    plt.ylabel('loss')
    plt.xlabel('epoch')
    
    plt.subplot(1, 2, 2)
    plt.plot(h.history['acc'])
    plt.plot(h.history['val_acc'])
    plt.legend(['acc', 'val_acc'])
    plt.ylabel('acc')
    plt.xlabel('epoch')

Out\[12\]:

Text(0.5,0,'epoch')

In \[14\]:

model.evaluate(X_train, y_train, batch_size=256)

7385/7385 [==============================] - 62s 8ms/step

Out\[14\]:

[0.7887161796288307, 0.7599187538037853]

In \[15\]:

model.evaluate(X_valid, y_valid, batch_size=256)

1008/1008 [==============================] - 9s 9ms/step

Out\[15\]:

[1.4538595203369382, 0.6448412698412699]

### 加载测试集 ###

In \[16\]:

df_test = pd.read_csv('test_a.csv', header=None)
    df_test.columns = ['image_id', 'class', 'x']
    del df_test['x']
    df_test.head()

Out\[16\]:

<table> 
 <thead> 
  <tr> 
   <th>&nbsp;</th> 
   <th>image_id</th> 
   <th>class</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>Images/collar_design_labels/60951f5761ea3a01d6...</td> 
   <td>collar_design_labels</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>Images/collar_design_labels/677e1183282769a3fe...</td> 
   <td>collar_design_labels</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>Images/collar_design_labels/3f5a2be205e5d253eb...</td> 
   <td>collar_design_labels</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>Images/collar_design_labels/1712bf3d7b57d779ad...</td> 
   <td>collar_design_labels</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>Images/collar_design_labels/0b2b4254f35ce3a41a...</td> 
   <td>collar_design_labels</td> 
  </tr> 
 </tbody> 
</table>

In \[17\]:

df_load = df_test[(df_test['class'] == cur_class)].copy()
    df_load.reset_index(inplace=True)
    del df_load['index']
    
    print('{0}: {1}'.format(cur_class, len(df_load)))
    df_load.head()

collar_design_labels: 1082

Out\[17\]:

In \[18\]:

n = len(df_load)
    X_test = np.zeros((n, width, width, 3), dtype=np.uint8)
    
    for i in tqdm(range(n)):
        X_test[i] = cv2.resize(cv2.imread('./test_a/{0}'.format(df_load['image_id'][i])), (width, width))

100%|██████████| 1082/1082 [00:04<00:00, 265.47it/s]

In \[19\]:

test_np = model.predict(X_test, batch_size=256)

In \[20\]:

test_np.shape

Out\[20\]:

(1082, 5)

In \[21\]:

result = []
    
    for i, row in df_load.iterrows():
        tmp_list = test_np[i]
        tmp_result = ''
        for tmp_ret in tmp_list:
            tmp_result += '{:.4f};'.format(tmp_ret)
            
        result.append(tmp_result[:-1])
    
    df_load['result'] = result
    df_load.head()

Out\[21\]:

<table> 
 <thead> 
  <tr> 
   <th>&nbsp;</th> 
   <th>image_id</th> 
   <th>class</th> 
   <th>result</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>Images/collar_design_labels/60951f5761ea3a01d6...</td> 
   <td>collar_design_labels</td> 
   <td>0.0008;0.9845;0.0005;0.0141;0.0001</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>Images/collar_design_labels/677e1183282769a3fe...</td> 
   <td>collar_design_labels</td> 
   <td>0.0000;0.0000;0.0000;1.0000;0.0000</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>Images/collar_design_labels/3f5a2be205e5d253eb...</td> 
   <td>collar_design_labels</td> 
   <td>0.0000;0.9998;0.0000;0.0002;0.0000</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>Images/collar_design_labels/1712bf3d7b57d779ad...</td> 
   <td>collar_design_labels</td> 
   <td>0.0056;0.0004;0.0087;0.9846;0.0007</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>Images/collar_design_labels/0b2b4254f35ce3a41a...</td> 
   <td>collar_design_labels</td> 
   <td>0.0000;0.0000;0.0002;0.9998;0.0000</td> 
  </tr> 
 </tbody> 
</table>

In \[22\]:

df_load.to_csv('./result/{}_0307a.csv'.format(prefix_cls), header=None, index=False)
    prefix_cls

Out\[22\]:

'collar'

[Link 1]: https://github.com/DavexPro/fashion-tag/blob/master/transfer_learning.ipynb