我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用keras.layers.core.Permute()。
def test_permute(): layer_test(core.Permute, kwargs={'dims': (2, 1)}, input_shape=(3, 2, 4))
def create_network(): # PixelCNN architecture, no pooling layer x = Input(batch_shape=(batch_size,n_channel,mnist_dim,mnist_dim)) # First layer using mask A x_ = Convolution2DNoFlip(*first_layer, input_shape=(1, 28, 28), border_mode='same', mask='A')(x) # Second type of layers using mask B for i in range(n_layer // 2): x_1 = Convolution2DNoFlip(*second_layer, activation='relu', border_mode='same', mask='B')(x_) x_2 = Convolution2DNoFlip(*second_layer, activation='relu', border_mode='same', mask='B')(x_1) if res_connections: x_ = merge([x_, x_2], mode='sum') else: x_ = x_2 # 2 layers of Relu followed by 1x1 conv x_ = Convolution2DNoFlip(64, 1, 1, activation='relu', border_mode='same', mask='B')(x_) x_ = Convolution2DNoFlip(128, 1, 1, activation='relu', border_mode='same', mask='B')(x_) # Depending on the output x_ = Convolution2DNoFlip(*third_layer,border_mode='same', mask='B')(x_) if MODE == '256ary': x_ = Reshape((256, mnist_dim**2))(x_) x_ = Permute((2,1))(x_) y = Activation(activation)(x_) model = Model(x, y) model.compile(optimizer='adagrad', loss=cost) print "Model compiled" return model
def Unet (nClasses , optimizer=None , input_width=360 , input_height=480 , nChannels=1 ): inputs = Input((nChannels, input_height, input_width)) conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(inputs) conv1 = Dropout(0.2)(conv1) conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv1) pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(pool1) conv2 = Dropout(0.2)(conv2) conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv2) pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(pool2) conv3 = Dropout(0.2)(conv3) conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv3) up1 = merge([UpSampling2D(size=(2, 2))(conv3), conv2], mode='concat', concat_axis=1) conv4 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(up1) conv4 = Dropout(0.2)(conv4) conv4 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv4) up2 = merge([UpSampling2D(size=(2, 2))(conv4), conv1], mode='concat', concat_axis=1) conv5 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(up2) conv5 = Dropout(0.2)(conv5) conv5 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv5) conv6 = Convolution2D(nClasses, 1, 1, activation='relu',border_mode='same')(conv5) conv6 = core.Reshape((nClasses,input_height*input_width))(conv6) conv6 = core.Permute((2,1))(conv6) conv7 = core.Activation('softmax')(conv6) model = Model(input=inputs, output=conv7) if not optimizer is None: model.compile(loss="categorical_crossentropy", optimizer= optimizer , metrics=['accuracy'] ) return model
def test_permute(self): """ Test the conversion of pooling layer. """ from keras.layers.core import Permute # Create a simple Keras model model = Sequential() model.add(Permute((3, 2, 1), input_shape=(10, 64,3))) input_names = ['input'] output_names = ['output'] spec = keras.convert(model, input_names, output_names).get_spec() self.assertIsNotNone(spec) # Test the model class self.assertIsNotNone(spec.description) self.assertTrue(spec.HasField('neuralNetwork')) # Test the inputs and outputs self.assertEquals(len(spec.description.input), len(input_names)) self.assertItemsEqual(input_names, map(lambda x: x.name, spec.description.input)) self.assertEquals(len(spec.description.output), len(output_names)) self.assertItemsEqual(output_names, map(lambda x: x.name, spec.description.output)) # Test the layer parameters. layers = spec.neuralNetwork.layers layer_0 = layers[0] self.assertIsNotNone(layer_0.permute)
def test_permute(self): """ Test the conversion of pooling layer. """ from keras.layers.core import Permute # Create a simple Keras model model = Sequential() model.add(Permute((3, 2, 1), input_shape=(10, 64,3))) input_names = ['input'] output_names = ['output'] spec = keras.convert(model, input_names, output_names).get_spec() self.assertIsNotNone(spec) # Test the model class self.assertIsNotNone(spec.description) self.assertTrue(spec.HasField('neuralNetwork')) # Test the inputs and outputs self.assertEquals(len(spec.description.input), len(input_names)) self.assertEqual(sorted(input_names), sorted(map(lambda x: x.name, spec.description.input))) self.assertEquals(len(spec.description.output), len(output_names)) self.assertEqual(sorted(output_names), sorted(map(lambda x: x.name, spec.description.output))) # Test the layer parameters. layers = spec.neuralNetwork.layers layer_0 = layers[0] self.assertIsNotNone(layer_0.permute)
def create_miml_model(base_model, L, K, name="miml"): """ Arguments: base_model (Sequential): A Neural Network in keras form (e.g. VGG, GoogLeNet) L (int): number of labels K (int): number of sub categories """ model = Sequential(layers=base_model.layers, name=name) # input: feature_map.shape = (n_bags, C, H, W) _, C, H, W = model.layers[-1].output_shape print("Creating miml... input feature_map.shape={},{},{}".format(C, H, W)) n_instances = H * W # shape -> (n_bags, (L * K), n_instances, 1) model.add(Convolution2D(L * K, 1, 1, name=MIML_FIRST_LAYER_NAME)) # shape -> (n_bags, L, K, n_instances) model.add(Reshape((L, K, n_instances), name=MIML_CUBE_LAYER_NAME)) # shape -> (n_bags, L, 1, n_instances) model.add(MaxPooling2D((K, 1), strides=(1, 1))) # softmax model.add(Reshape((L, n_instances))) model.add(Permute((2, 1))) model.add(Activation("softmax")) model.add(Permute((2, 1))) model.add(Reshape((L, 1, n_instances), name=MIML_TABLE_LAYER_NAME)) # shape -> (n_bags, L, 1, 1) model.add(MaxPooling2D((1, n_instances), strides=(1, 1))) # shape -> (n_bags, L) model.add(Reshape((L,), name=MIML_OUTPUT_LAYER_NAME)) return model
def bottleneck(inp, output, internal_scale=4, asymmetric=0, dilated=0, downsample=False, dropout_rate=0.1): # main branch internal = output // internal_scale encoder = inp # 1x1 input_stride = 2 if downsample else 1 # the 1st 1x1 projection is replaced with a 2x2 convolution when downsampling encoder = Conv2D(internal, (input_stride, input_stride), # padding='same', strides=(input_stride, input_stride), use_bias=False)(encoder) # Batch normalization + PReLU encoder = BatchNormalization(momentum=0.1)(encoder) # enet uses momentum of 0.1, keras default is 0.99 encoder = PReLU(shared_axes=[1, 2])(encoder) # conv if not asymmetric and not dilated: encoder = Conv2D(internal, (3, 3), padding='same')(encoder) elif asymmetric: encoder = Conv2D(internal, (1, asymmetric), padding='same', use_bias=False)(encoder) encoder = Conv2D(internal, (asymmetric, 1), padding='same')(encoder) elif dilated: encoder = Conv2D(internal, (3, 3), dilation_rate=(dilated, dilated), padding='same')(encoder) else: raise(Exception('You shouldn\'t be here')) encoder = BatchNormalization(momentum=0.1)(encoder) # enet uses momentum of 0.1, keras default is 0.99 encoder = PReLU(shared_axes=[1, 2])(encoder) # 1x1 encoder = Conv2D(output, (1, 1), use_bias=False)(encoder) encoder = BatchNormalization(momentum=0.1)(encoder) # enet uses momentum of 0.1, keras default is 0.99 encoder = SpatialDropout2D(dropout_rate)(encoder) other = inp # other branch if downsample: other = MaxPooling2D()(other) other = Permute((1, 3, 2))(other) pad_feature_maps = output - inp.get_shape().as_list()[3] tb_pad = (0, 0) lr_pad = (0, pad_feature_maps) other = ZeroPadding2D(padding=(tb_pad, lr_pad))(other) other = Permute((1, 3, 2))(other) encoder = add([encoder, other]) encoder = PReLU(shared_axes=[1, 2])(encoder) return encoder
def bottleneck(inp, output, internal_scale=4, asymmetric=0, dilated=0, downsample=False, dropout_rate=0.1): # main branch internal = output // internal_scale encoder = inp # 1x1 input_stride = 2 if downsample else 1 # the 1st 1x1 projection is replaced with a 2x2 convolution when downsampling encoder = Conv2D(internal, (input_stride, input_stride), # padding='same', strides=(input_stride, input_stride), use_bias=False)(encoder) # Batch normalization + PReLU encoder = BatchNormalization(momentum=0.1)(encoder) # enet_unpooling uses momentum of 0.1, keras default is 0.99 encoder = PReLU(shared_axes=[1, 2])(encoder) # conv if not asymmetric and not dilated: encoder = Conv2D(internal, (3, 3), padding='same')(encoder) elif asymmetric: encoder = Conv2D(internal, (1, asymmetric), padding='same', use_bias=False)(encoder) encoder = Conv2D(internal, (asymmetric, 1), padding='same')(encoder) elif dilated: encoder = Conv2D(internal, (3, 3), dilation_rate=(dilated, dilated), padding='same')(encoder) else: raise(Exception('You shouldn\'t be here')) encoder = BatchNormalization(momentum=0.1)(encoder) # enet_unpooling uses momentum of 0.1, keras default is 0.99 encoder = PReLU(shared_axes=[1, 2])(encoder) # 1x1 encoder = Conv2D(output, (1, 1), use_bias=False)(encoder) encoder = BatchNormalization(momentum=0.1)(encoder) # enet_unpooling uses momentum of 0.1, keras default is 0.99 encoder = SpatialDropout2D(dropout_rate)(encoder) other = inp # other branch if downsample: other, indices = MaxPoolingWithArgmax2D()(other) other = Permute((1, 3, 2))(other) pad_feature_maps = output - inp.get_shape().as_list()[3] tb_pad = (0, 0) lr_pad = (0, pad_feature_maps) other = ZeroPadding2D(padding=(tb_pad, lr_pad))(other) other = Permute((1, 3, 2))(other) encoder = add([encoder, other]) encoder = PReLU(shared_axes=[1, 2])(encoder) if downsample: return encoder, indices else: return encoder
def build_keras_model_score_word_sg(index_size,vector_size, #vocab_size, context_size, #code_dim, score_vector_size, sub_batch_size=256, word_vectors=None, score_vectors=None, hidden_vectors=None, model=None ): """ >>> word_vectors=np.array([[1,2,-1,1],[3,4,-1,-2],[5,6,-2,-2]]) >>> score_vectors=np.array([[10,20,11,21,5,6,7,8],[30,40,33,41,9,8,7,6]]) >>> hidden_vectors=np.array([[1,0,1,1],[0,1,1,1]]) >>> sub_batch_size=3 >>> vector_size=4 >>> score_vector_size=2 >>> kerasmodel=build_keras_model_score_word_sg(index_size=3,vector_size=vector_size,context_size=2,score_vector_size=score_vector_size,sub_batch_size=sub_batch_size,word_vectors=word_vectors,score_vectors=score_vectors,hidden_vectors=hidden_vectors) >>> ind=[[0,1,2],[1,2,0]] >>> ipt=[[1,0,1],[0,1,0]] >>> tmp1=kerasmodel.predict({'index':np.array(ind),'point':np.array(ipt)}) >>> tmp3=np.array([[score_vectors[ipt[i][j]].reshape((score_vector_size,vector_size)).dot(word_vectors[ind[i][j]]) for j in range(sub_batch_size) ] for i in range(2)]) >>> tmp2=np.array([[word_vectors[ind[i][j]].dot(hidden_vectors[ipt[i][j]].T) for j in range(sub_batch_size) ] for i in range(2)]) >>> np.linalg.norm(1/(1+np.exp(-tmp2))-tmp1['code'])+np.linalg.norm(tmp1['score']-tmp3) < 0.0001 True """ kerasmodel = Graph() kerasmodel.add_input(name='point' , input_shape=(sub_batch_size,), dtype=int) kerasmodel.add_input(name='index' , input_shape=(sub_batch_size,), dtype=int) if word_vectors is None: kerasmodel.add_node(Embedding(index_size, vector_size, input_length=sub_batch_size ),name='embedding', input='index') else: kerasmodel.add_node(Embedding(index_size, vector_size, input_length=sub_batch_size,weights=[word_vectors]),name='embedding', input='index') if hidden_vectors is None: kerasmodel.add_node(Embedding(context_size, vector_size, input_length=sub_batch_size ),name='embedpoint', input='point') else: kerasmodel.add_node(Embedding(context_size, vector_size, input_length=sub_batch_size,weights=[hidden_vectors]),name='embedpoint', input='point') kerasmodel.add_node(Lambda(lambda x:x.sum(2)) , name='merge',inputs=['embedding','embedpoint'], merge_mode='mul') kerasmodel.add_node(Activation('sigmoid'), name='sigmoid', input='merge') kerasmodel.add_output(name='code',input='sigmoid') if score_vectors is None: kerasmodel.add_node(Embedding(context_size, score_vector_size*vector_size, input_length=sub_batch_size, ),name='embedscore', input='point') else: kerasmodel.add_node(Embedding(context_size, score_vector_size*vector_size, input_length=sub_batch_size,weights=[score_vectors]),name='embedscore', input='point') kerasmodel.add_node(Reshape((sub_batch_size,score_vector_size,vector_size,)) , name='score1',input='embedscore') kerasmodel.add_node(Flatten(), name='index1',input='embedding') kerasmodel.add_node(RepeatVector(score_vector_size), name='index2',input='index1') kerasmodel.add_node(Reshape((score_vector_size,sub_batch_size,vector_size,)) , name='index3',input='index2') kerasmodel.add_node(Permute((2,1,3,)) , name='index4',input='index3') kerasmodel.add_node(Lambda(lambda x:x.sum(-1)) , name='scorenode',inputs=['score1','index4'], merge_mode='mul') kerasmodel.add_output(name='score',input='scorenode') kerasmodel.compile('rmsprop', {'code':'mse','score':'mse'}) return kerasmodel
