inception_v4.py 文件源码

def inception_v4(num_classes, dropout_keep_prob, weights, include_top):
    '''
    Creates the inception v4 network

    Args:
        num_classes: number of classes
        dropout_keep_prob: float, the fraction to keep before final layer.

    Returns: 
        logits: the logits outputs of the model.
    '''

    # Input Shape is 299 x 299 x 3 (tf) or 3 x 299 x 299 (th)
    if K.image_data_format() == 'channels_first':
        inputs = Input((3, 299, 299))
    else:
        inputs = Input((299, 299, 3))

    # Make inception base
    x = inception_v4_base(inputs)


    # Final pooling and prediction
    if include_top:
        # 1 x 1 x 1536
        x = AveragePooling2D((8,8), padding='valid')(x)
        x = Dropout(dropout_keep_prob)(x)
        x = Flatten()(x)
        # 1536
        x = Dense(units=num_classes, activation='softmax')(x)

    model = Model(inputs, x, name='inception_v4')

    # load weights
    if weights == 'imagenet':
        if K.image_data_format() == 'channels_first':
            if K.backend() == 'tensorflow':
                warnings.warn('You are using the TensorFlow backend, yet you '
                              'are using the Theano '
                              'image data format convention '
                              '(`image_data_format="channels_first"`). '
                              'For best performance, set '
                              '`image_data_format="channels_last"` in '
                              'your Keras config '
                              'at ~/.keras/keras.json.')
        if include_top:
            weights_path = get_file(
                'inception-v4_weights_tf_dim_ordering_tf_kernels.h5',
                WEIGHTS_PATH,
                cache_subdir='models',
                md5_hash='9fe79d77f793fe874470d84ca6ba4a3b')
        else:
            weights_path = get_file(
                'inception-v4_weights_tf_dim_ordering_tf_kernels_notop.h5',
                WEIGHTS_PATH_NO_TOP,
                cache_subdir='models',
                md5_hash='9296b46b5971573064d12e4669110969')
        model.load_weights(weights_path, by_name=True)
    return model