这篇教程Python layers.average方法代码示例写得很实用，希望能帮到您。
本文整理汇总了Python中keras.layers.average方法的典型用法代码示例。如果您正苦于以下问题：Python layers.average方法的具体用法？Python layers.average怎么用？Python layers.average使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块keras.layers的用法示例。 在下文中一共展示了layers.average方法的9个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。 示例1: test_dense_elementwise_params# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import average [as 别名]def test_dense_elementwise_params(self): options = dict(modes=[add, multiply, concatenate, average, maximum]) def build_model(mode): x1 = Input(shape=(3,)) x2 = Input(shape=(3,)) y1 = Dense(4)(x1) y2 = Dense(4)(x2) z = mode([y1, y2]) model = Model([x1, x2], z) return mode, model product = itertools.product(*options.values()) args = [build_model(p[0]) for p in product] print("Testing a total of %s cases. This could take a while" % len(args)) for param, model in args: self._run_test(model, param)
示例2: test_merge_average# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import average [as 别名]def test_merge_average(): i1 = layers.Input(shape=(4, 5)) i2 = layers.Input(shape=(4, 5)) o = layers.average([i1, i2]) assert o._keras_shape == (None, 4, 5) model = models.Model([i1, i2], o) avg_layer = layers.Average() o2 = avg_layer([i1, i2]) assert avg_layer.output_shape == (None, 4, 5) x1 = np.random.random((2, 4, 5)) x2 = np.random.random((2, 4, 5)) out = model.predict([x1, x2]) assert out.shape == (2, 4, 5) assert_allclose(out, 0.5 * (x1 + x2), atol=1e-4)
开发者ID:hello-sea，项目名称:DeepLearning_Wavelet-LSTM，代码行数:18，代码来源:merge_test.py
示例3: test_imdb_fasttext_first_2# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import average [as 别名]def test_imdb_fasttext_first_2(self): max_features = 10 max_len = 6 embedding_dims = 4 pool_length = 2 model = Sequential() model.add(Embedding(max_features, embedding_dims, input_length=max_len)) # we add a AveragePooling1D, which will average the embeddings # of all words in the document model.add(AveragePooling1D(pool_size=pool_length)) self._test_model(model, one_dim_seq_flags=[True])
示例4: fconcatenate# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import average [as 别名]def fconcatenate(path_orig, path_down): if path_orig._keras_shape == path_down._keras_shape: path_down_cropped = path_down else: crop_x_1 = int(np.ceil((path_down._keras_shape[2] - path_orig._keras_shape[2]) / 2)) crop_x_0 = path_down._keras_shape[2] - path_orig._keras_shape[2] - crop_x_1 crop_y_1 = int(np.ceil((path_down._keras_shape[3] - path_orig._keras_shape[3]) / 2)) crop_y_0 = path_down._keras_shape[3] - path_orig._keras_shape[3] - crop_y_1 crop_z_1 = int(np.ceil((path_down._keras_shape[4] - path_orig._keras_shape[4]) / 2)) crop_z_0 = path_down._keras_shape[4] - path_orig._keras_shape[4] - crop_z_1 path_down_cropped = Cropping3D(cropping=((crop_x_0, crop_x_1), (crop_y_0, crop_y_1), (crop_z_0, crop_z_1)))(path_down) connected = average([path_orig, path_down_cropped]) return connected
示例5: two_stream_fuse# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import average [as 别名]def two_stream_fuse(self): # spatial stream (frozen) cnn_spatial_multi = self.cnn_spatial_multi() # temporal stream (frozen) cnn_temporal_multi = self.cnn_temporal_multi() # fused by taking average outputs = average([cnn_spatial_multi.output, cnn_temporal_multi.output]) model = Model([cnn_spatial_multi.input, cnn_temporal_multi.input], outputs) return model # CNN model for the temporal stream with multiple inputs
示例6: cnn_spatial# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import average [as 别名]def cnn_spatial(self): base_model = InceptionV3(weights='imagenet', include_top=False) # add a global spatial average pooling layer x = base_model.output x = GlobalAveragePooling2D()(x) # let's add a fully-connected layer x = Dense(1024, activation='relu')(x) # and a logistic layer predictions = Dense(self.nb_classes, activation='softmax')(x) model = Model(inputs=base_model.input, outputs=predictions) return model # CNN model for the temporal stream
示例7: eltwise# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import average [as 别名]def eltwise(layer, layer_in, layerId): out = {} if (layer['params']['layer_type'] == 'Multiply'): # This input reverse is to handle visualization out[layerId] = multiply(layer_in[::-1]) elif (layer['params']['layer_type'] == 'Sum'): out[layerId] = add(layer_in[::-1]) elif (layer['params']['layer_type'] == 'Average'): out[layerId] = average(layer_in[::-1]) elif (layer['params']['layer_type'] == 'Dot'): out[layerId] = dot(layer_in[::-1], -1) else: out[layerId] = maximum(layer_in[::-1]) return out
示例8: fCreateModel_SPP_MultiPath# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import average [as 别名]def fCreateModel_SPP_MultiPath(patchSize, patchSize2, dr_rate=0.0, iPReLU=0, l2_reg=1e-6): # Total params: 2,057,510 # There are 2 pathway, whose receptive fields are in multiple relation. # Their outputs are averaged as the final prediction # The third down sampling convolutional layer in each pathway is replaced by the SPP module Strides = fgetStrides() kernelnumber = fgetKernelNumber() sharedConv1 = fCreateVNet_Block sharedDown1 = fCreateVNet_DownConv_Block sharedConv2 = fCreateVNet_Block sharedDown2 = fCreateVNet_DownConv_Block sharedConv3 = fCreateVNet_Block sharedSPP = fSPP inp1 = Input(shape=(1, patchSize[0], patchSize[1], patchSize[2])) inp1_Conv_1 = sharedConv1(inp1, kernelnumber[0], type=fgetLayerNumConv(), l2_reg=l2_reg) inp1_DownConv_1 = sharedDown1(inp1_Conv_1, inp1_Conv_1._keras_shape[1], Strides[0], iPReLU=iPReLU, dr_rate=dr_rate, l2_reg=l2_reg) inp1_Conv_2 = sharedConv2(inp1_DownConv_1, kernelnumber[1], type=fgetLayerNumConv(), l2_reg=l2_reg) inp1_DownConv_2 = sharedDown2(inp1_Conv_2, inp1_Conv_2._keras_shape[1], Strides[1], iPReLU=iPReLU, dr_rate=dr_rate, l2_reg=l2_reg) inp1_Conv_3 = sharedConv3(inp1_DownConv_2, kernelnumber[2], type=fgetLayerNumConv(), l2_reg=l2_reg) inp1_SPP = sharedSPP(inp1_Conv_3, level=3) inp2 = Input(shape=(1, patchSize2[0], patchSize2[1], patchSize2[2])) inp2_Conv_1 = sharedConv1(inp2, kernelnumber[0], type=fgetLayerNumConv(), l2_reg=l2_reg) inp2_DownConv_1 = sharedDown1(inp2_Conv_1, inp2_Conv_1._keras_shape[1], Strides[0], iPReLU=iPReLU, dr_rate=dr_rate, l2_reg=l2_reg) inp2_Conv_2 = sharedConv2(inp2_DownConv_1, kernelnumber[1], type=fgetLayerNumConv(), l2_reg=l2_reg) inp2_DownConv_2 = sharedDown2(inp2_Conv_2, inp2_Conv_2._keras_shape[1], Strides[1], iPReLU=iPReLU, dr_rate=dr_rate, l2_reg=l2_reg) inp2_Conv_3 = sharedConv3(inp2_DownConv_2, kernelnumber[2], type=fgetLayerNumConv(), l2_reg=l2_reg) inp2_SPP = sharedSPP(inp2_Conv_3, level=3) SPP_aver = average([inp1_SPP, inp2_SPP]) dropout_out = Dropout(dr_rate)(SPP_aver) dense_out = Dense(units=2, kernel_initializer='normal', kernel_regularizer=l2(l2_reg))(dropout_out) output_fc = Activation('softmax')(dense_out) model_shared = Model(inputs=[inp1, inp2], outputs = output_fc) return model_shared
示例9: fCreateModel_FCN_MultiFM# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import average [as 别名]def fCreateModel_FCN_MultiFM(patchSize, dr_rate=0.0, iPReLU=0,l1_reg=0, l2_reg=1e-6): # Total params: 1,420,549 # The dense layer is repleced by a convolutional layer with filters=2 for the two classes # The FM from the third down scaled convolutional layer is upsempled by deconvolution and # added with the FM from the second down scaled convolutional layer. # The combined FM goes through a convolutional layer with filters=2 for the two classes # The two predictions are averages as the final result. Strides = fgetStrides() kernelnumber = fgetKernelNumber() inp = Input(shape=(1, int(patchSize[0]), int(patchSize[1]), int(patchSize[2]))) after_Conv_1 = fCreateVNet_Block(inp, kernelnumber[0], type=fgetLayerNumConv(), l2_reg=l2_reg) after_DownConv_1 = fCreateVNet_DownConv_Block(after_Conv_1, after_Conv_1._keras_shape[1], Strides[0], iPReLU=iPReLU, dr_rate=dr_rate, l2_reg=l2_reg) after_Conv_2 = fCreateVNet_Block(after_DownConv_1, kernelnumber[1], type=fgetLayerNumConv(), l2_reg=l2_reg) after_DownConv_2 = fCreateVNet_DownConv_Block(after_Conv_2, after_Conv_2._keras_shape[1], Strides[1], iPReLU=iPReLU, dr_rate=dr_rate, l2_reg=l2_reg) after_Conv_3 = fCreateVNet_Block(after_DownConv_2, kernelnumber[2], type=fgetLayerNumConv(), l2_reg=l2_reg) after_DownConv_3 = fCreateVNet_DownConv_Block(after_Conv_3, after_Conv_3._keras_shape[1], Strides[2], iPReLU=iPReLU, dr_rate=dr_rate, l2_reg=l2_reg) # fully convolution over the FM from the deepest level dropout_out1 = Dropout(dr_rate)(after_DownConv_3) fclayer1 = Conv3D(2, kernel_size=(1,1,1), kernel_initializer='he_normal', weights=None, padding='valid', strides=(1, 1, 1), kernel_regularizer=l1_l2(l1_reg, l2_reg), )(dropout_out1) fclayer1 = GlobalAveragePooling3D()(fclayer1) # Upsample FM from the deepest level, add with FM from level 2, UpedFM_Level3 = Conv3DTranspose(filters=97, kernel_size=(3,3,1), strides=(2,2,1), padding='same')(after_DownConv_3) conbined_FM_Level23 = add([UpedFM_Level3, after_DownConv_2]) fclayer2 = Conv3D(2, kernel_size=(1,1,1), kernel_initializer='he_normal', weights=None, padding='valid', strides=(1, 1, 1), kernel_regularizer=l1_l2(l1_reg, l2_reg), )(conbined_FM_Level23) fclayer2 = GlobalAveragePooling3D()(fclayer2) # combine the two predictions using average fcl_aver = average([fclayer1, fclayer2]) predict = Activation('softmax')(fcl_aver) cnn_fcl_msfm = Model(inputs=inp, outputs=predict) return cnn_fcl_msfm
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