自学教程：Python layers.AveragePooling3D方法代码示例

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling3D [as 别名]def transition_layer_3D(input_tensor, numFilters, compressionFactor=1.0):    numOutPutFilters = int(numFilters*compressionFactor)    if K.image_data_format() == 'channels_last':        bn_axis = -1    else:        bn_axis = 1    x = BatchNormalization(axis=bn_axis)(input_tensor)    x = Activation('relu')(x)    x = Conv3D(numOutPutFilters, (1, 1, 1), strides=(1, 1, 1), padding='same', kernel_initializer='he_normal')(x)    # downsampling    x = AveragePooling3D((2, 2, 2), strides=(2, 2, 2), padding='valid', data_format='channels_last', name='')(x)    return x, numOutPutFilters 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling3D [as 别名]def inception3D(image_size, num_labels):    num_channels=1    inputs = Input(shape = (image_size, image_size, image_size, num_channels))    m = Convolution3D(32, 5, 5, 5, subsample=(1, 1, 1), activation='relu', border_mode='valid', input_shape=())(inputs)    m = MaxPooling3D(pool_size=(2, 2, 2), strides=None, border_mode='same')(m)    # inception module 0    branch1x1 = Convolution3D(32, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(m)    branch3x3_reduce = Convolution3D(32, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(m)    branch3x3 = Convolution3D(64, 3, 3, 3, subsample=(1, 1, 1), activation='relu', border_mode='same')(branch3x3_reduce)    branch5x5_reduce = Convolution3D(16, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(m)    branch5x5 = Convolution3D(32, 5, 5, 5, subsample=(1, 1, 1), activation='relu', border_mode='same')(branch5x5_reduce)    branch_pool = MaxPooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), border_mode='same')(m)    branch_pool_proj = Convolution3D(32, 1, 1, 1, subsample=(1, 1, 1), activation='relu', border_mode='same')(branch_pool)    #m = merge([branch1x1, branch3x3, branch5x5, branch_pool_proj], mode='concat', concat_axis=-1)    from keras.layers import concatenate    m = concatenate([branch1x1, branch3x3, branch5x5, branch_pool_proj],axis=-1)    m = AveragePooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), border_mode='valid')(m)    m = Flatten()(m)    m = Dropout(0.7)(m)    # expliciately seperate Dense and Activation layers in order for projecting to structural feature space    m = Dense(num_labels, activation='linear')(m)    m = Activation('softmax')(m)    mod = KM.Model(input=inputs, output=m)    return mod 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling3D [as 别名]def define_model(image_shape):    img_input = Input(shape=image_shape)    x = Convolution3D(16, 3, 3, 3, subsample=(1, 1, 1), border_mode='same')(img_input)    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)    x = res_block(x, nb_filters=32, block=1, subsample_factor=2)    x = res_block(x, nb_filters=32, block=1, subsample_factor=1)    x = res_block(x, nb_filters=32, block=1, subsample_factor=1)    x = res_block(x, nb_filters=64, block=2, subsample_factor=2)    x = res_block(x, nb_filters=64, block=2, subsample_factor=1)    x = res_block(x, nb_filters=64, block=2, subsample_factor=1)    x = res_block(x, nb_filters=64, block=2, subsample_factor=1)    x = res_block(x, nb_filters=128, block=3, subsample_factor=2)    x = res_block(x, nb_filters=128, block=3, subsample_factor=1)    x = res_block(x, nb_filters=128, block=3, subsample_factor=1)    x = res_block(x, nb_filters=128, block=3, subsample_factor=1)    x = res_block(x, nb_filters=256, block=4, subsample_factor=2)    x = res_block(x, nb_filters=256, block=4, subsample_factor=1)    x = res_block(x, nb_filters=256, block=4, subsample_factor=1)    x = res_block(x, nb_filters=256, block=4, subsample_factor=1)    x = BatchNormalization(axis=4)(x)    x = Activation('relu')(x)    x = AveragePooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2), border_mode='valid')(x)    x = Flatten()(x)    x = Dense(1, activation='sigmoid', name='predictions')(x)    model = Model(img_input, x)    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy', 'precision', 'recall', 'fmeasure'])    model.summary()    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling3D [as 别名]def define_model():    img_input = Input(shape=(64, 64, 64, 1))    x = Convolution3D(16, 3, 3, 3, subsample=(1, 1, 1), border_mode='same')(img_input)    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)    x = res_block(x, nb_filters=32, block=1, subsample_factor=2)    x = res_block(x, nb_filters=32, block=1, subsample_factor=1)    x = res_block(x, nb_filters=32, block=1, subsample_factor=1)    x = res_block(x, nb_filters=64, block=2, subsample_factor=2)    x = res_block(x, nb_filters=64, block=2, subsample_factor=1)    x = res_block(x, nb_filters=64, block=2, subsample_factor=1)    x = res_block(x, nb_filters=64, block=2, subsample_factor=1)    x = res_block(x, nb_filters=128, block=3, subsample_factor=2)    x = res_block(x, nb_filters=128, block=3, subsample_factor=1)    x = res_block(x, nb_filters=128, block=3, subsample_factor=1)    x = res_block(x, nb_filters=128, block=3, subsample_factor=1)    x = res_block(x, nb_filters=256, block=4, subsample_factor=2)    x = res_block(x, nb_filters=256, block=4, subsample_factor=1)    x = res_block(x, nb_filters=256, block=4, subsample_factor=1)    x = res_block(x, nb_filters=256, block=4, subsample_factor=1)    x = BatchNormalization(axis=4)(x)    x = Activation('relu')(x)    x = AveragePooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2), border_mode='valid')(x)    x = Flatten()(x)    x = Dense(1, activation='sigmoid', name='predictions')(x)    model = Model(img_input, x)    model.compile(optimizer='adam', loss='binary_crossentropy')    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling3D [as 别名]def define_model():    img_input = Input(shape=(32, 32, 64, 1))    x = Convolution3D(16, 5, 5, 5, subsample=(1, 1, 1), border_mode='same')(img_input)    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)    x = res_block(x, nb_filters=16, block=0, subsample_factor=1)    x = res_block(x, nb_filters=32, block=1, subsample_factor=2)    x = res_block(x, nb_filters=32, block=1, subsample_factor=1)    x = res_block(x, nb_filters=32, block=1, subsample_factor=1)    x = res_block(x, nb_filters=64, block=2, subsample_factor=2)    x = res_block(x, nb_filters=64, block=2, subsample_factor=1)    x = res_block(x, nb_filters=64, block=2, subsample_factor=1)    x = res_block(x, nb_filters=128, block=3, subsample_factor=2)    x = res_block(x, nb_filters=128, block=3, subsample_factor=1)    x = res_block(x, nb_filters=128, block=3, subsample_factor=1)    x = BatchNormalization(axis=4)(x)    x = Activation('relu')(x)    x = AveragePooling3D(pool_size=(4, 4, 8))(x)    x = Flatten()(x)    x = Dense(1, activation='sigmoid', name='predictions')(x)    model = Model(img_input, x)    model.compile(optimizer='adam', loss='binary_crossentropy')    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling3D [as 别名]def graph_embedding(tensor, n_layers, n_avg_size, n_kernel_size, t_kernel_size, n_max_size, t_max_size):    """    Graph embedding.    :param tensor:    :param n_layers:    :return:    """    input_shape = K.int_shape(tensor)    _, n_odes, n_timesteps, side_dim, side_dim, n_channels_in = input_shape    # hide temporal dimension    tensor = TransposeLayer((0, 2, 1, 3, 4, 5))(tensor)  # (None, 64, 100, 7, 7, 1024)    tensor = ReshapeLayer((n_odes, side_dim, side_dim, n_channels_in))(tensor)    # pool over node    tensor = AveragePooling3D(pool_size=(n_avg_size, 1, 1), name='pool_n')(tensor)    _, n_odes, side_dim, side_dim, n_channels_in = K.int_shape(tensor)    # recover node dimension    tensor = ReshapeLayer((n_timesteps, n_odes, side_dim, side_dim, n_channels_in))(tensor)  # (None, 64, 100, 7, 7, 1024)    tensor = TransposeLayer((0, 2, 1, 3, 4, 5))(tensor)  # (None, 100, 64, 7, 7, 1024)    # hide the node dimension    tensor = ReshapeLayer((n_timesteps, side_dim, side_dim, n_channels_in))(tensor)  # (None, 64, 7, 7, 1024)    # 2 layers spatio-temporal conv    for i in range(n_layers):        layer_id = '%d' % (i + 1)        # spatial conv        tensor = Conv3D(n_channels_in, (1, 1, 1), padding='SAME', name='conv_s_%s' % (layer_id))(tensor)  # (None, 64, 7, 7, 1024)        # temporal conv        tensor = DepthwiseConv1DLayer(t_kernel_size, padding='SAME', name='conv_t_%s' % (layer_id))(tensor)  # (None, 64, 7, 7, 1024)        # node conv        tensor = __convolve_nodes(tensor, n_odes, layer_id, n_kernel_size)  # (None, 100, 7, 7, 1024)        # activation        tensor = BatchNormalization()(tensor)        tensor = LeakyReLU(alpha=0.2)(tensor)        # max_pool over nodes        tensor = MaxPooling3D(pool_size=(n_max_size, 1, 1), name='pool_n_%s' % (layer_id))(tensor)  # (None, 100, 7, 7, 1024)        _, n_odes, side_dim, side_dim, n_channels_in = K.int_shape(tensor)        # get back temporal dimension and hide node dimension        tensor = ReshapeLayer((n_timesteps, n_odes, side_dim, side_dim, n_channels_in))(tensor)  # (None, 64, 100, 7, 7, 1024)        tensor = TransposeLayer((0, 2, 1, 3, 4, 5))(tensor)  # (None, 100, 64, 7, 7, 1024)        tensor = ReshapeLayer((n_timesteps, side_dim, side_dim, n_channels_in))(tensor)  # (None, 64, 7, 7, 1024)        # max_pool over time        tensor = MaxPooling3D(pool_size=(t_max_size, 1, 1), name='pool_t_%s' % (layer_id))(tensor)  # (None, 64, 7, 7, 1024)        _, n_timesteps, side_dim, side_dim, n_channels_in = K.int_shape(tensor)  # (None, 64, 7, 7, 1024)    # recover nodes dimension    tensor = ReshapeLayer((n_odes, n_timesteps, side_dim, side_dim, n_channels_in))(tensor)    return tensor 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling3D [as 别名]def get_net(input_shape=(CUBE_SIZE, CUBE_SIZE, CUBE_SIZE, 1), load_weight_path=None, features=False, mal=False) -> Model:    inputs = Input(shape=input_shape, name="input_1")    x = inputs    x = AveragePooling3D(pool_size=(2, 1, 1), strides=(2, 1, 1), border_mode="same")(x)    x = Convolution3D(64, 3, 3, 3, activation='relu', border_mode='same', name='conv1', subsample=(1, 1, 1))(x)    x = MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), border_mode='valid', name='pool1')(x)    # 2nd layer group    x = Convolution3D(128, 3, 3, 3, activation='relu', border_mode='same', name='conv2', subsample=(1, 1, 1))(x)    x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool2')(x)    if USE_DROPOUT:        x = Dropout(p=0.3)(x)    # 3rd layer group    x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3a', subsample=(1, 1, 1))(x)    x = Convolution3D(256, 3, 3, 3, activation='relu', border_mode='same', name='conv3b', subsample=(1, 1, 1))(x)    x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool3')(x)    if USE_DROPOUT:        x = Dropout(p=0.4)(x)    # 4th layer group    x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4a', subsample=(1, 1, 1))(x)    x = Convolution3D(512, 3, 3, 3, activation='relu', border_mode='same', name='conv4b', subsample=(1, 1, 1),)(x)    x = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), border_mode='valid', name='pool4')(x)    if USE_DROPOUT:        x = Dropout(p=0.5)(x)    last64 = Convolution3D(64, 2, 2, 2, activation="relu", name="last_64")(x)    out_class = Convolution3D(1, 1, 1, 1, activation="sigmoid", name="out_class_last")(last64)    out_class = Flatten(name="out_class")(out_class)    out_malignancy = Convolution3D(1, 1, 1, 1, activation=None, name="out_malignancy_last")(last64)    out_malignancy = Flatten(name="out_malignancy")(out_malignancy)    model = Model(input=inputs, output=[out_class, out_malignancy])    if load_weight_path is not None:        model.load_weights(load_weight_path, by_name=False)    model.compile(optimizer=SGD(lr=LEARN_RATE, momentum=0.9, nesterov=True), loss={"out_class": "binary_crossentropy", "out_malignancy": mean_absolute_error}, metrics={"out_class": [binary_accuracy, binary_crossentropy], "out_malignancy": mean_absolute_error})    if features:        model = Model(input=inputs, output=[last64])    model.summary(line_length=140)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import AveragePooling3D [as 别名]def pooling(layer, layer_in, layerId, tensor=True):    poolMap = {        ('1D', 'MAX'): MaxPooling1D,        ('2D', 'MAX'): MaxPooling2D,        ('3D', 'MAX'): MaxPooling3D,        ('1D', 'AVE'): AveragePooling1D,        ('2D', 'AVE'): AveragePooling2D,        ('3D', 'AVE'): AveragePooling3D,    }    out = {}    layer_type = layer['params']['layer_type']    pool_type = layer['params']['pool']    padding = get_padding(layer)    if (layer_type == '1D'):        strides = layer['params']['stride_w']        kernel = layer['params']['kernel_w']        if (padding == 'custom'):            p_w = layer['params']['pad_w']            out[layerId + 'Pad'] = ZeroPadding1D(padding=p_w)(*layer_in)            padding = 'valid'            layer_in = [out[layerId + 'Pad']]    elif (layer_type == '2D'):        strides = (layer['params']['stride_h'], layer['params']['stride_w'])        kernel = (layer['params']['kernel_h'], layer['params']['kernel_w'])        if (padding == 'custom'):            p_h, p_w = layer['params']['pad_h'], layer['params']['pad_w']            out[layerId + 'Pad'] = ZeroPadding2D(padding=(p_h, p_w))(*layer_in)            padding = 'valid'            layer_in = [out[layerId + 'Pad']]    else:        strides = (layer['params']['stride_h'], layer['params']['stride_w'],                   layer['params']['stride_d'])        kernel = (layer['params']['kernel_h'], layer['params']['kernel_w'],                  layer['params']['kernel_d'])        if (padding == 'custom'):            p_h, p_w, p_d = layer['params']['pad_h'], layer['params']['pad_w'],/                layer['params']['pad_d']            out[layerId +                'Pad'] = ZeroPadding3D(padding=(p_h, p_w, p_d))(*layer_in)            padding = 'valid'            layer_in = [out[layerId + 'Pad']]    # Note - figure out a permanent fix for padding calculation of layers    # in case padding is given in layer attributes    # if ('padding' in layer['params']):    #    padding = layer['params']['padding']    out[layerId] = poolMap[(layer_type, pool_type)](        pool_size=kernel, strides=strides, padding=padding)    if tensor:        out[layerId] = out[layerId](*layer_in)    return out# ********** Locally-connected Layers **********