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本文整理汇总了Python中keras.layers.UpSampling3D方法的典型用法代码示例。如果您正苦于以下问题：Python layers.UpSampling3D方法的具体用法？Python layers.UpSampling3D怎么用？Python layers.UpSampling3D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.UpSampling3D方法的8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_keras_import# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import UpSampling3D [as 别名]def test_keras_import(self): # Upsample 1D model = Sequential() model.add(UpSampling1D(size=2, input_shape=(16, 1))) model.build() self.keras_param_test(model, 0, 2) # Upsample 2D model = Sequential() model.add(UpSampling2D(size=(2, 2), input_shape=(16, 16, 1))) model.build() self.keras_param_test(model, 0, 3) # Upsample 3D model = Sequential() model.add(UpSampling3D(size=(2, 2, 2), input_shape=(16, 16, 16, 1))) model.build() self.keras_param_test(model, 0, 4)# ********** Pooling Layers **********
示例2: nn_architecture_seg_3d# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import UpSampling3D [as 别名]def nn_architecture_seg_3d(input_shape, pool_size=(2, 2, 2), n_labels=1, initial_learning_rate=0.00001, depth=3, n_base_filters=16, metrics=dice_coefficient, batch_normalization=True): inputs = Input(input_shape) current_layer = inputs levels = list() for layer_depth in range(depth): layer1 = create_convolution_block(input_layer=current_layer, n_filters=n_base_filters * (2**layer_depth), batch_normalization=batch_normalization) layer2 = create_convolution_block(input_layer=layer1, n_filters=n_base_filters * (2**layer_depth) * 2, batch_normalization=batch_normalization) if layer_depth < depth - 1: current_layer = MaxPooling3D(pool_size=pool_size)(layer2) levels.append([layer1, layer2, current_layer]) else: current_layer = layer2 levels.append([layer1, layer2]) for layer_depth in range(depth - 2, -1, -1): up_convolution = UpSampling3D(size=pool_size) concat = concatenate([up_convolution, levels[layer_depth][1]], axis=1) current_layer = create_convolution_block(n_filters=levels[layer_depth][1]._keras_shape[1], input_layer=concat, batch_normalization=batch_normalization) current_layer = create_convolution_block(n_filters=levels[layer_depth][1]._keras_shape[1], input_layer=current_layer, batch_normalization=batch_normalization) final_convolution = Conv3D(n_labels, (1, 1, 1))(current_layer) act = Activation('sigmoid')(final_convolution) model = Model(inputs=inputs, outputs=act) if not isinstance(metrics, list): metrics = [metrics] model.compile(optimizer=Adam(lr=initial_learning_rate), loss=dice_coefficient_loss, metrics=metrics) return model
示例3: model_simple_upsampling__reshape# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import UpSampling3D [as 别名]def model_simple_upsampling__reshape(img_shape, class_n=None): from keras.layers import Input, Dense, Convolution3D, MaxPooling3D, UpSampling3D, Reshape, Flatten from keras.models import Sequential, Model from keras.layers.core import Activation from aitom.classify.deep.unsupervised.autoencoder.seg_util import conv_block NUM_CHANNELS=1 input_shape = (None, img_shape[0], img_shape[1], img_shape[2], NUM_CHANNELS) # use relu activation for hidden layer to guarantee non-negative outputs are passed to the max pooling layer. In such case, as long as the output layer is linear activation, the network can still accomodate negative image intendities, just matter of shift back using the bias term input_img = Input(shape=input_shape[1:]) x = input_img x = conv_block(x, 32, 3, 3, 3) x = MaxPooling3D((2, 2, 2), border_mode='same')(x) x = conv_block(x, 32, 3, 3, 3) x = MaxPooling3D((2, 2, 2), border_mode='same')(x) x = conv_block(x, 32, 3, 3, 3) x = UpSampling3D((2, 2, 2))(x) x = conv_block(x, 32, 3, 3, 3) x = UpSampling3D((2, 2, 2))(x) x = conv_block(x, 32, 3, 3, 3) x = Convolution3D(class_n, 1, 1, 1, border_mode='same')(x) x = Reshape((N.prod(img_shape), class_n))(x) x = Activation('softmax')(x) model = Model(input=input_img, output=x) print('model layers:') for l in model.layers: print (l.output_shape, l.name) return model
示例4: create_up_sampling_module# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import UpSampling3D [as 别名]def create_up_sampling_module(input_layer, n_filters, size=(2, 2, 2)): up_sample = UpSampling3D(size=size)(input_layer) convolution = create_convolution_block(up_sample, n_filters) return convolution
示例5: get_up_convolution# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import UpSampling3D [as 别名]def get_up_convolution(n_filters, pool_size, kernel_size=(2, 2, 2), strides=(2, 2, 2), deconvolution=False): if deconvolution: return Deconvolution3D(filters=n_filters, kernel_size=kernel_size, strides=strides) else: return UpSampling3D(size=pool_size)
开发者ID:ellisdg，项目名称:3DUnetCNN，代码行数:9，代码来源:unet.py
示例6: test_keras_export# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import UpSampling3D [as 别名]def test_keras_export(self): tests = open(os.path.join(settings.BASE_DIR, 'tests', 'unit', 'keras_app', 'keras_export_test.json'), 'r') response = json.load(tests) tests.close() net = yaml.safe_load(json.dumps(response['net'])) net = {'l0': net['Input'], 'l1': net['Input2'], 'l2': net['Input4'], 'l3': net['Upsample']} # Conv 1D net['l1']['connection']['output'].append('l3') net['l3']['connection']['input'] = ['l1'] net['l3']['params']['layer_type'] = '1D' inp = data(net['l1'], '', 'l1')['l1'] temp = upsample(net['l3'], [inp], 'l3') model = Model(inp, temp['l3']) self.assertEqual(model.layers[1].__class__.__name__, 'UpSampling1D') # Conv 2D net['l0']['connection']['output'].append('l0') net['l3']['connection']['input'] = ['l0'] net['l3']['params']['layer_type'] = '2D' inp = data(net['l0'], '', 'l0')['l0'] temp = upsample(net['l3'], [inp], 'l3') model = Model(inp, temp['l3']) self.assertEqual(model.layers[1].__class__.__name__, 'UpSampling2D') # Conv 3D net['l2']['connection']['output'].append('l3') net['l3']['connection']['input'] = ['l2'] net['l3']['params']['layer_type'] = '3D' inp = data(net['l2'], '', 'l2')['l2'] temp = upsample(net['l3'], [inp], 'l3') model = Model(inp, temp['l3']) self.assertEqual(model.layers[1].__class__.__name__, 'UpSampling3D')
示例7: auto_classifier_model# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import UpSampling3D [as 别名]def auto_classifier_model(img_shape, encoding_dim=128, NUM_CHANNELS=1, num_of_class=2): input_shape = (None, img_shape[0], img_shape[1], img_shape[2], NUM_CHANNELS) mask_shape = (None, num_of_class) # use relu activation for hidden layer to guarantee non-negative outputs are passed to the max pooling layer. In such case, as long as the output layer is linear activation, the network can still accomodate negative image intendities, just matter of shift back using the bias term input_img = Input(shape=input_shape[1:]) mask = Input(shape=mask_shape[1:]) x = input_img x = conv_block(x, 32, 3, 3, 3) x = MaxPooling3D((2, 2, 2), padding ='same')(x) x = conv_block(x, 32, 3, 3, 3) x = MaxPooling3D((2, 2, 2), padding ='same')(x) encoder_conv_shape = [_.value for _ in x.get_shape()] # x.get_shape() returns a list of tensorflow.python.framework.tensor_shape.Dimension objects x = Flatten()(x) encoded = Dense(encoding_dim, activation='relu', activity_regularizer=regularizers.l1(10e-5))(x) encoder = Model(inputs=input_img, outputs=encoded) x = BatchNormalization()(x) x = Dense(encoding_dim, activation='relu', activity_regularizer=regularizers.l1(10e-5))(x) x = Dense(128, activation = 'relu')(x) x = Dense(num_of_class, activation = 'softmax')(x) prob = x # classifier output classifier = Model(inputs=input_img, outputs=prob) input_img_decoder = Input(shape=encoder.output_shape[1:]) x = input_img_decoder x = Dense(np.prod(encoder_conv_shape[1:]), activation='relu')(x) x = Reshape(encoder_conv_shape[1:])(x) x = UpSampling3D((2, 2, 2))(x) x = conv_block(x, 32, 3, 3, 3) x = UpSampling3D((2, 2, 2))(x) x = conv_block(x, 32, 3, 3, 3) x = Convolution3D(1, (3, 3, 3), activation='linear', padding ='same')(x) decoded = x # autoencoder output decoder = Model(inputs=input_img_decoder, outputs=decoded) autoencoder = Sequential() for l in encoder.layers: autoencoder.add(l) last = None for l in decoder.layers: last = l autoencoder.add(l) decoded = autoencoder(input_img) auto_classifier = Model(inputs=input_img, outputs=[decoded, prob]) auto_classifier.summary() return auto_classifier
示例8: _build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import UpSampling3D [as 别名]def _build(self): # get parameters proj = self.proj_params proj_axis = axes_dict(self.config.axes)[proj.axis] # define surface projection network (3D -> 2D) inp = u = Input(self.config.unet_input_shape) def conv_layers(u): for _ in range(proj.n_conv_per_depth): u = Conv3D(proj.n_filt, proj.kern, padding='same', activation='relu')(u) return u # down for _ in range(proj.n_depth): u = conv_layers(u) u = MaxPooling3D(proj.pool)(u) # middle u = conv_layers(u) # up for _ in range(proj.n_depth): u = UpSampling3D(proj.pool)(u) u = conv_layers(u) u = Conv3D(1, proj.kern, padding='same', activation='linear')(u) # convert learned features along Z to surface probabilities # (add 1 to proj_axis because of batch dimension in tensorflow) u = Lambda(lambda x: softmax(x, axis=1+proj_axis))(u) # multiply Z probabilities with Z values in input stack u = Multiply()([inp, u]) # perform surface projection by summing over weighted Z values u = Lambda(lambda x: K.sum(x, axis=1+proj_axis))(u) model_projection = Model(inp, u) # define denoising network (2D -> 2D) # (remove projected axis from input_shape) input_shape = list(self.config.unet_input_shape) del input_shape[proj_axis] model_denoising = nets.common_unet( n_dim = self.config.n_dim-1, n_channel_out = self.config.n_channel_out, prob_out = self.config.probabilistic, residual = self.config.unet_residual, n_depth = self.config.unet_n_depth, kern_size = self.config.unet_kern_size, n_first = self.config.unet_n_first, last_activation = self.config.unet_last_activation, )(tuple(input_shape)) # chain models together return Model(inp, model_denoising(model_projection(inp)))
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