我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用keras.layers.AveragePooling3D()。
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}) model.compile(optimizer=SGD(lr=LEARN_RATE, momentum=0.9, nesterov=True), loss={"out_class": "binary_crossentropy"}, metrics={"out_class": [binary_accuracy, binary_crossentropy]}) if features: model = Model(input=inputs, output=[last64]) model.summary(line_length=140) return model
def define_model(image_shape): img_input = Input(shape=image_shape) 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', metrics=['accuracy', 'precision', 'recall', 'fmeasure']) model.summary() return model
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
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
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
def test_delete_channels_averagepooling3d(channel_index, data_format): layer = AveragePooling3D([2, 3, 2], data_format=data_format) layer_test_helper_flatten_3d(layer, channel_index, data_format=data_format)
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
def ecog_3d_model(channels=None, weights=None): input_tensor = Input(shape=(1,8,8, 1000)) # Block 1 x = AveragePooling3D((1, 1, 5), name='pre_pool')(input_tensor) x = Convolution3D(4, 2, 2, 3, border_mode='same', name='block1_conv1')(x) # x = BatchNormalization(axis=1)(x) x = Activation('relu')(x) x = MaxPooling3D((2, 2, 3), name='block1_pool')(x) # Block 2 x = Convolution3D(8, 2, 2, 3, border_mode='same', name='block2_conv1')(x) # x = BatchNormalization(axis=1)(x) x = Activation('relu')(x) x = MaxPooling3D(( 1, 1, 3), name='block2_pool')(x) # Block 3 x = Convolution3D(16, 2,2, 3, border_mode='same', name='block3_conv1')(x) # x = BatchNormalization(axis=1)(x) x = Activation('relu')(x) x = MaxPooling3D((1, 1, 2), name='block3_pool')(x) # Block 4 # x = Convolution2D(32, 1, 3, border_mode='same', name='block4_conv1')(x) # x = BatchNormalization(axis=1)(x) # x = Activation('relu')(x) # x = MaxPooling2D((1, 2), name='block4_pool')(x) x = Flatten(name='flatten')(x) x = Dropout(0.5)(x) x = Dense(64, W_regularizer=l2(0.01), name='fc1')(x) #x = BatchNormalization()(x) #x = Activation('relu')(x) #x = Dropout(0.5)(x) #x = Dense(1, name='predictions')(x) # x = BatchNormalization()(x) predictions = Activation('sigmoid')(x) # for layer in base_model.layers[:10]: # layer.trainable = False model = Model(input=input_tensor, output=predictions) if weights is not None: model.load_weights(weights) return model
def pooling(layer, layer_in, layerId): 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']] out[layerId] = poolMap[(layer_type, pool_type)](pool_size=kernel, strides=strides, padding=padding)( *layer_in) return out # ********** Locally-connected Layers **********
