我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用keras.backend.is_keras_tensor()。
def test_jaccard_distance(): # all_right, almost_right, half_right, all_wrong y_true = np.array([[0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1., 0.]]) y_pred = np.array([[0, 0, 1, 0], [0, 0, 0.9, 0], [0, 0, 0.1, 0], [1, 1, 0.1, 1.]]) r = jaccard_distance( K.variable(y_true), K.variable(y_pred), ) if K.is_keras_tensor(r): assert K.int_shape(r) == (4, ) all_right, almost_right, half_right, all_wrong = K.eval(r) assert all_right == 0, 'should converge on zero' assert all_right < almost_right assert almost_right < half_right assert half_right < all_wrong
def nn_base(input_tensor=None, trainable=False): # Determine proper input shape if K.image_dim_ordering() == 'th': input_shape = (3, None, None) else: input_shape = (None, None, 3) if input_tensor is None: img_input = Input(shape=input_shape) else: if not K.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor # Block 1 x = Convolution2D(96, (7, 7), strides=(2, 2), activation='relu', padding='valid', name='block1_conv1')(img_input) x = MaxPooling2D((3, 3), strides=(2, 2), name='block1_pool')(x) # Block 2 x = Convolution2D(256, (5, 5), strides=(2, 2), activation='relu', padding='same', name='block2_conv1')(x) x = MaxPooling2D((3, 3), strides=(2, 2), name='block2_pool')(x) # Block 3 x = Convolution2D(384, (3, 3), strides=(1, 1), activation='relu', padding='same', name='block3_conv1')(x) x = Convolution2D(384, (3, 3), strides=(1, 1), activation='relu', padding='same', name='block3_conv2')(x) x = Convolution2D(384, (3, 3), strides=(1, 1), activation='relu', padding='same', name='block3_conv3')(x) return x
def nn_base(input_tensor=None, trainable=False): # Determine proper input shape if K.image_dim_ordering() == 'th': input_shape = (3, None, None) else: input_shape = (None, None, 3) if input_tensor is None: img_input = Input(shape=input_shape) else: if not K.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor if K.image_dim_ordering() == 'tf': bn_axis = 3 else: bn_axis = 1 x = ZeroPadding2D((3, 3))(img_input) x = Convolution2D(64, (7, 7), strides=(2, 2), name='conv1', trainable = trainable)(x) x = FixedBatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = Activation('relu')(x) x = MaxPooling2D((3, 3), strides=(2, 2))(x) x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), trainable = trainable) x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', trainable = trainable) x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', trainable = trainable) x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', trainable = trainable) x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', trainable = trainable) x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', trainable = trainable) x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', trainable = trainable) x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', trainable = trainable) x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', trainable = trainable) x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', trainable = trainable) x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', trainable = trainable) x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', trainable = trainable) x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', trainable = trainable) return x
def nn_base(input_tensor=None, trainable=False): # Determine proper input shape if K.image_dim_ordering() == 'th': input_shape = (3, None, None) else: input_shape = (None, None, 3) if input_tensor is None: img_input = Input(shape=input_shape) else: if not K.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor # Block 1 x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input) x = Convolution2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # Block 2 x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x) x = Convolution2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) # Block 3 x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x) x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x) x = Convolution2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) # Block 4 x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x) x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x) x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) # Block 5 x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x) x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x) x = Convolution2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x) return x
def nn_base(input_tensor=None, trainable=False): # Determine proper input shape if K.image_dim_ordering() == 'th': input_shape = (3, None, None) else: input_shape = (None, None, 3) if input_tensor is None: img_input = Input(shape=input_shape) else: if not K.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor if K.image_dim_ordering() == 'tf': bn_axis = 3 else: bn_axis = 1 # Block 1 x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input) x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) # Block 2 x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x) x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x) # Block 3 x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x) x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x) # Block 4 x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x) x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x) # Block 5 x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x) x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x) # x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x) return x
def densenet_cifar10_model(logits=False, input_range_type=1, pre_filter=lambda x:x): assert input_range_type == 1 batch_size = 64 nb_classes = 10 img_rows, img_cols = 32, 32 img_channels = 3 img_dim = (img_channels, img_rows, img_cols) if K.image_dim_ordering() == "th" else (img_rows, img_cols, img_channels) depth = 40 nb_dense_block = 3 growth_rate = 12 nb_filter = 16 dropout_rate = 0.0 # 0.0 for data augmentation input_tensor = None include_top=True if logits is True: activation = None else: activation = "softmax" # Determine proper input shape input_shape = _obtain_input_shape(img_dim, default_size=32, min_size=8, data_format=K.image_data_format(), include_top=include_top) if input_tensor is None: img_input = Input(shape=input_shape) else: if not K.is_keras_tensor(input_tensor): img_input = Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor x = __create_dense_net(nb_classes, img_input, True, depth, nb_dense_block, growth_rate, nb_filter, -1, False, 0.0, dropout_rate, 1E-4, activation) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = Model(inputs, x, name='densenet') return model # Source: https://github.com/titu1994/DenseNet
