DL之VGG16：基于VGG16迁移技术实现猫狗分类识别(图片数据量调整→保存h5模型)

女爷i 2022-12-23 03:29 329阅读 0赞

**目录**

基于VGG16迁移技术实现猫狗分类识别(图片数据量调整→保存h5模型)

设计思路

输出结果

1488/1488 4s 2ms/step - loss: 0.0139 - acc: 0.9966 - val\_loss: 1.0130 - val\_acc: 0.8762

2000/2000 6s 3ms/step - loss: 0.0122 - acc: 0.9950 - val\_loss: 0.7943 - val\_acc: 0.9125

4000/4000 10s 2ms/step - loss: 0.0216 - acc: 0.9945 - val\_loss: 0.9137 - val\_acc: 0.8812

6000/6000 15s 2ms/step - loss: 0.0262 - acc: 0.9933 - val\_loss: 0.6805 - val\_acc: 0.8988

6992/6992 20s 3ms/step - loss: 0.0395 - acc: 0.9894 - val\_loss: 0.6387 - val\_acc: 0.8975

99980/99980 237s 2ms/step - loss: 0.0598 - acc: 0.9922 - val\_loss: 1.2305 - val\_acc: 0.9100

核心代码

--------------------

**相关文章**  
[DL之VGG16：基于VGG16迁移技术实现猫狗分类识别(图片数据量调整→保存h5模型)][DL_VGG16_VGG16_h5]  
[DL之VGG16：基于VGG16迁移技术实现猫狗分类识别(图片数据量调整→保存h5模型)实现][DL_VGG16_VGG16_h5 1]

# 基于VGG16迁移技术实现猫狗分类识别(图片数据量调整→保存h5模型) #

## 设计思路 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70][]

## 输出结果 ##

Using TensorFlow backend.
    F:\Program Files\Python\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
      _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
    F:\Program Files\Python\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
      _np_qint32 = np.dtype([("qint32", np.int32, 1)])
    F:\Program Files\Python\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
      np_resource = np.dtype([("resource", np.ubyte, 1)])
    
    
    None
    2020-11-22 13:40:03.693339: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
    Found 17500 images belonging to 2 classes.
    Found 7500 images belonging to 2 classes.
    -------------显示网络结构--------------------
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    input_2 (InputLayer)         (None, 4, 4, 512)         0         
    _________________________________________________________________
    flatten_1 (Flatten)          (None, 8192)              0         
    _________________________________________________________________
    dense_1 (Dense)              (None, 256)               2097408   
    _________________________________________________________________
    activation_1 (Activation)    (None, 256)               0         
    _________________________________________________________________
    dropout_1 (Dropout)          (None, 256)               0         
    _________________________________________________________________
    dense_2 (Dense)              (None, 1)                 257       
    _________________________________________________________________
    activation_2 (Activation)    (None, 1)                 0         
    =================================================================
    Total params: 2,097,665
    Trainable params: 2,097,665
    Non-trainable params: 0
    _________________________________________________________________
    None

### 1488/1488 4s 2ms/step - loss: 0.0139 - acc: 0.9966 - val\_loss: 1.0130 - val\_acc: 0.8762 ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 1][]

### 2000/2000 6s 3ms/step - loss: 0.0122 - acc: 0.9950 - val\_loss: 0.7943 - val\_acc: 0.9125 ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 2][]

### 4000/4000 10s 2ms/step - loss: 0.0216 - acc: 0.9945 - val\_loss: 0.9137 - val\_acc: 0.8812 ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 3][]

### 6000/6000 15s 2ms/step - loss: 0.0262 - acc: 0.9933 - val\_loss: 0.6805 - val\_acc: 0.8988 ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 4][]

### 6992/6992 20s 3ms/step - loss: 0.0395 - acc: 0.9894 - val\_loss: 0.6387 - val\_acc: 0.8975 ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 5][]

### 99980/99980 237s 2ms/step - loss: 0.0598 - acc: 0.9922 - val\_loss: 1.2305 - val\_acc: 0.9100 ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 6][]

## 核心代码 ##

def VGG16(include_top=True,
              weights='imagenet',
              input_tensor=None,
              input_shape=None,
              pooling=None,
              classes=1000,
              **kwargs):
        """Instantiates the VGG16 architecture.
    
        Optionally loads weights pre-trained on ImageNet.
        Note that the data format convention used by the model is
        the one specified in your Keras config at `~/.keras/keras.json`.
    
        # Arguments
            include_top: whether to include the 3 fully-connected
                layers at the top of the network.
            weights: one of `None` (random initialization),
                  'imagenet' (pre-training on ImageNet),
                  or the path to the weights file to be loaded.
            input_tensor: optional Keras tensor
                (i.e. output of `layers.Input()`)
                to use as image input for the model.
            input_shape: optional shape tuple, only to be specified
                if `include_top` is False (otherwise the input shape
                has to be `(224, 224, 3)`
                (with `channels_last` data format)
                or `(3, 224, 224)` (with `channels_first` data format).
                It should have exactly 3 input channels,
                and width and height should be no smaller than 32.
                E.g. `(200, 200, 3)` would be one valid value.
            pooling: Optional pooling mode for feature extraction
                when `include_top` is `False`.
                - `None` means that the output of the model will be
                    the 4D tensor output of the
                    last convolutional block.
                - `avg` means that global average pooling
                    will be applied to the output of the
                    last convolutional block, and thus
                    the output of the model will be a 2D tensor.
                - `max` means that global max pooling will
                    be applied.
            classes: optional number of classes to classify images
                into, only to be specified if `include_top` is True, and
                if no `weights` argument is specified.
    
        # Returns
            A Keras model instance.
    
        # Raises
            ValueError: in case of invalid argument for `weights`,
                or invalid input shape.
        """
        backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)
    
        if not (weights in {'imagenet', None} or os.path.exists(weights)):
            raise ValueError('The `weights` argument should be either '
                             '`None` (random initialization), `imagenet` '
                             '(pre-training on ImageNet), '
                             'or the path to the weights file to be loaded.')
    
        if weights == 'imagenet' and include_top and classes != 1000:
            raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
                             ' as true, `classes` should be 1000')
        # Determine proper input shape
        input_shape = _obtain_input_shape(input_shape,
                                          default_size=224,
                                          min_size=32,
                                          data_format=backend.image_data_format(),
                                          require_flatten=include_top,
                                          weights=weights)
    
        if input_tensor is None:
            img_input = layers.Input(shape=input_shape)
        else:
            if not backend.is_keras_tensor(input_tensor):
                img_input = layers.Input(tensor=input_tensor, shape=input_shape)
            else:
                img_input = input_tensor
        # Block 1
        x = layers.Conv2D(64, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block1_conv1')(img_input)
        x = layers.Conv2D(64, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block1_conv2')(x)
        x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
    
        # Block 2
        x = layers.Conv2D(128, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block2_conv1')(x)
        x = layers.Conv2D(128, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block2_conv2')(x)
        x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
    
        # Block 3
        x = layers.Conv2D(256, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block3_conv1')(x)
        x = layers.Conv2D(256, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block3_conv2')(x)
        x = layers.Conv2D(256, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block3_conv3')(x)
        x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
    
        # Block 4
        x = layers.Conv2D(512, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block4_conv1')(x)
        x = layers.Conv2D(512, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block4_conv2')(x)
        x = layers.Conv2D(512, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block4_conv3')(x)
        x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
    
        # Block 5
        x = layers.Conv2D(512, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block5_conv1')(x)
        x = layers.Conv2D(512, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block5_conv2')(x)
        x = layers.Conv2D(512, (3, 3),
                          activation='relu',
                          padding='same',
                          name='block5_conv3')(x)
        x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
    
        if include_top:
            # Classification block
            x = layers.Flatten(name='flatten')(x)
            x = layers.Dense(4096, activation='relu', name='fc1')(x)
            x = layers.Dense(4096, activation='relu', name='fc2')(x)
            x = layers.Dense(classes, activation='softmax', name='predictions')(x)
        else:
            if pooling == 'avg':
                x = layers.GlobalAveragePooling2D()(x)
            elif pooling == 'max':
                x = layers.GlobalMaxPooling2D()(x)
    
        # Ensure that the model takes into account
        # any potential predecessors of `input_tensor`.
        if input_tensor is not None:
            inputs = keras_utils.get_source_inputs(input_tensor)
        else:
            inputs = img_input
        # Create model.
        model = models.Model(inputs, x, name='vgg16')
    
        # Load weights.
        if weights == 'imagenet':
            if include_top:
                weights_path = keras_utils.get_file(
                    'vgg16_weights_tf_dim_ordering_tf_kernels.h5',
                    WEIGHTS_PATH,
                    cache_subdir='models',
                    file_hash='64373286793e3c8b2b4e3219cbf3544b')
            else:
                weights_path = keras_utils.get_file(
                    'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
                    WEIGHTS_PATH_NO_TOP,
                    cache_subdir='models',
                    file_hash='6d6bbae143d832006294945121d1f1fc')
            model.load_weights(weights_path)
            if backend.backend() == 'theano':
                keras_utils.convert_all_kernels_in_model(model)
        elif weights is not None:
            model.load_weights(weights)
    
        return model
    
    
    
    input_node = Input(shape=feature_size)
    z = input_node
    z = Flatten()(z)
    z = Dense(256)(z)
    z = Activation('relu')(z)
    z = Dropout(0.5)(z)
    z = Dense(1)(z)
    z = Activation('sigmoid')(z)

448/2000 [=====>........................] - ETA: 4s - loss: 0.0556 - acc: 0.9866
     464/2000 [=====>........................] - ETA: 4s - loss: 0.0537 - acc: 0.9871
     496/2000 [======>.......................] - ETA: 4s - loss: 0.0505 - acc: 0.9879
     528/2000 [======>.......................] - ETA: 3s - loss: 0.0475 - acc: 0.9886
     560/2000 [=======>......................] - ETA: 3s - loss: 0.0448 - acc: 0.9893
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     624/2000 [========>.....................] - ETA: 3s - loss: 0.0422 - acc: 0.9888
     656/2000 [========>.....................] - ETA: 3s - loss: 0.0414 - acc: 0.9878
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     752/2000 [==========>...................] - ETA: 3s - loss: 0.0368 - acc: 0.9894
     784/2000 [==========>...................] - ETA: 3s - loss: 0.0353 - acc: 0.9898
     816/2000 [===========>..................] - ETA: 3s - loss: 0.0339 - acc: 0.9902
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    2000/2000 [==============================] - 6s 3ms/step - loss: 0.0345 - acc: 0.9895 - val_loss: 0.7680 - val_acc: 0.9025
    Epoch 36/50
    
      16/2000 [..............................] - ETA: 9s - loss: 4.0149e-04 - acc: 1.0000
      32/2000 [..............................] - ETA: 8s - loss: 3.0761e-04 - acc: 1.0000
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    2000/2000 [==============================] - 6s 3ms/step - loss: 0.0148 - acc: 0.9945 - val_loss: 0.7079 - val_acc: 0.9025
    Epoch 37/50
    
      16/2000 [..............................] - ETA: 4s - loss: 0.0014 - acc: 1.0000
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    2000/2000 [==============================] - 5s 3ms/step - loss: 0.0217 - acc: 0.9935 - val_loss: 0.7360 - val_acc: 0.9062
    Epoch 38/50
    
      16/2000 [..............................] - ETA: 5s - loss: 5.3922e-04 - acc: 1.0000
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    2000/2000 [==============================] - 5s 3ms/step - loss: 0.0162 - acc: 0.9935 - val_loss: 0.7347 - val_acc: 0.9050
    Epoch 39/50
    
      16/2000 [..............................] - ETA: 4s - loss: 3.9777e-05 - acc: 1.0000
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    1968/2000 [============================>.] - ETA: 0s - loss: 0.0161 - acc: 0.9934
    2000/2000 [==============================] - 6s 3ms/step - loss: 0.0158 - acc: 0.9935 - val_loss: 0.7254 - val_acc: 0.9100
    Epoch 40/50
    
      16/2000 [..............................] - ETA: 5s - loss: 1.1826e-07 - acc: 1.0000
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    2000/2000 [==============================] - 7s 3ms/step - loss: 0.0279 - acc: 0.9930 - val_loss: 0.7294 - val_acc: 0.9100
    Epoch 41/50
    
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    2000/2000 [==============================] - 6s 3ms/step - loss: 0.0194 - acc: 0.9940 - val_loss: 0.7656 - val_acc: 0.9025
    Epoch 42/50
    
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    2000/2000 [==============================] - 6s 3ms/step - loss: 0.0122 - acc: 0.9950 - val_loss: 0.7119 - val_acc: 0.9087
    Epoch 43/50
    
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    2000/2000 [==============================] - 6s 3ms/step - loss: 0.0272 - acc: 0.9935 - val_loss: 0.6954 - val_acc: 0.9113
    Epoch 44/50
    
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    2000/2000 [==============================] - 5s 3ms/step - loss: 0.0193 - acc: 0.9945 - val_loss: 0.7085 - val_acc: 0.9075
    Epoch 45/50
    
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    2000/2000 [==============================] - 6s 3ms/step - loss: 0.0168 - acc: 0.9940 - val_loss: 0.7875 - val_acc: 0.9025
    Epoch 46/50
    
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    2000/2000 [==============================] - 5s 3ms/step - loss: 0.0143 - acc: 0.9980 - val_loss: 0.8773 - val_acc: 0.9012
    Epoch 47/50
    
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    2000/2000 [==============================] - 6s 3ms/step - loss: 0.0236 - acc: 0.9950 - val_loss: 0.9994 - val_acc: 0.8988
    Epoch 48/50
    
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    2000/2000 [==============================] - 6s 3ms/step - loss: 0.0154 - acc: 0.9950 - val_loss: 0.7933 - val_acc: 0.8975
    Epoch 49/50
    
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    2000/2000 [==============================] - 6s 3ms/step - loss: 0.0115 - acc: 0.9965 - val_loss: 0.7285 - val_acc: 0.9113
    Epoch 50/50
    
      16/2000 [..............................] - ETA: 4s - loss: 2.8943e-05 - acc: 1.0000
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    2000/2000 [==============================] - 6s 3ms/step - loss: 0.0122 - acc: 0.9950 - val_loss: 0.7943 - val_acc: 0.9125

[DL_VGG16_VGG16_h5]: https://yunyaniu.blog.csdn.net/article/details/109934857
[DL_VGG16_VGG16_h5 1]: https://yunyaniu.blog.csdn.net/article/details/109957480
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