Python layers.Reshape方法代码示例

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def build_generator(self):

        model = Sequential()

        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
        model.add(Reshape((7, 7, 128)))
        model.add(BatchNormalization(momentum=0.8))
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=3, padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=3, padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Conv2D(1, kernel_size=3, padding="same"))
        model.add(Activation("tanh"))

        model.summary()

        noise = Input(shape=(self.latent_dim,))
        img = model(noise)

        return Model(noise, img) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def build_generator(self):
        model = Sequential()

        model.add(Dense(512, input_dim=self.latent_dim))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(np.prod(self.img_shape), activation='tanh'))
        model.add(Reshape(self.img_shape))

        model.summary()

        z = Input(shape=(self.latent_dim,))
        gen_img = model(z)

        return Model(z, gen_img) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def build_generator(self):

        model = Sequential()

        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
        model.add(Reshape((7, 7, 128)))
        model.add(BatchNormalization(momentum=0.8))
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=3, padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=3, padding="same"))
        model.add(Activation("relu"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Conv2D(self.channels, kernel_size=3, padding='same'))
        model.add(Activation("tanh"))

        gen_input = Input(shape=(self.latent_dim,))
        img = model(gen_input)

        model.summary()

        return Model(gen_input, img) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def build_generator(self):

        model = Sequential()

        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
        model.add(Reshape((7, 7, 128)))
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=4, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=4, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(Conv2D(self.channels, kernel_size=4, padding="same"))
        model.add(Activation("tanh"))

        model.summary()

        noise = Input(shape=(self.latent_dim,))
        img = model(noise)

        return Model(noise, img) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def build_generator(self):

        model = Sequential()

        model.add(Dense(256, input_dim=self.latent_dim))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(1024))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(np.prod(self.img_shape), activation='tanh'))
        model.add(Reshape(self.img_shape))

        model.summary()

        noise = Input(shape=(self.latent_dim,))
        img = model(noise)

        return Model(noise, img) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def build_generator(self):

        model = Sequential()

        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
        model.add(Reshape((7, 7, 128)))
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=3, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(Conv2D(self.channels, kernel_size=3, padding="same"))
        model.add(Activation("tanh"))

        model.summary()

        noise = Input(shape=(self.latent_dim,))
        img = model(noise)

        return Model(noise, img) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def duc(x, factor=8, output_shape=(512, 512, 1)):
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1
    H, W, c, r = output_shape[0], output_shape[1], output_shape[2], factor
    h = H / r
    w = W / r
    x = Conv2D(
            c*r*r,
            (3, 3),
            padding='same',
            name='conv_duc_%s'%factor)(x)
    x = BatchNormalization(axis=bn_axis,name='bn_duc_%s'%factor)(x)
    x = Activation('relu')(x)
    x = Permute((3, 1, 2))(x)
    x = Reshape((c, r, r, h, w))(x)
    x = Permute((1, 4, 2, 5, 3))(x)
    x = Reshape((c, H, W))(x)
    x = Permute((2, 3, 1))(x)

    return x


# interpolation 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def get_Shared_Model(input_dim):
    sharedNet = Sequential()
    sharedNet.add(Dense(128, input_shape=(input_dim,), activation='relu'))
    sharedNet.add(Dropout(0.1))
    sharedNet.add(Dense(128, activation='relu'))
    sharedNet.add(Dropout(0.1))
    sharedNet.add(Dense(128, activation='relu'))
    # sharedNet.add(Dropout(0.1))
    # sharedNet.add(Dense(3, activation='relu'))
    # sharedNet = Sequential()
    # sharedNet.add(Dense(4096, activation="tanh", kernel_regularizer=l2(2e-3)))
    # sharedNet.add(Reshape(target_shape=(64, 64, 1)))
    # sharedNet.add(Conv2D(filters=64, kernel_size=3, strides=(2, 2), padding="same", activation="relu", kernel_regularizer=l2(1e-3)))
    # sharedNet.add(MaxPooling2D())
    # sharedNet.add(Conv2D(filters=128, kernel_size=3, strides=(2, 2), padding="same", activation="relu", kernel_regularizer=l2(1e-3)))
    # sharedNet.add(MaxPooling2D())
    # sharedNet.add(Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding="same", activation="relu", kernel_regularizer=l2(1e-3)))
    # sharedNet.add(Flatten())
    # sharedNet.add(Dense(1024, activation="sigmoid", kernel_regularizer=l2(1e-3)))
    return sharedNet 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def call(self, inputs):
        def wrapper(rois, mrcnn_class, mrcnn_bbox, image_meta):
            # currently supports one image per batch
            b = 0
            _, _, window, _ = parse_image_meta(image_meta)
            detections = refine_detections(
                rois[b], mrcnn_class[b], mrcnn_bbox[b], window[b], self.config)
            # Pad with zeros if detections < DETECTION_MAX_INSTANCES
            gap = self.config.DETECTION_MAX_INSTANCES - detections.shape[0]
            assert gap >= 0
            if gap > 0:
                detections = np.pad(detections, [(0, gap), (0, 0)],
                                    'constant', constant_values=0)

            # Cast to float32
            # TODO: track where float64 is introduced
            detections = detections.astype(np.float32)

            # Reshape output
            # [batch, num_detections, (y1, x1, y2, x2, class_score)] in pixels
            return np.reshape(detections,
                              [1, self.config.DETECTION_MAX_INSTANCES, 6])

        # Return wrapped function
        return tf.py_func(wrapper, inputs, tf.float32) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def model(self, block_starting_size=128,num_blocks=4):
        model = Sequential()
        
        block_size = block_starting_size 
        model.add(Dense(block_size, input_shape=(self.LATENT_SPACE_SIZE,)))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))

        for i in range(num_blocks-1):
            block_size = block_size * 2
            model.add(Dense(block_size))
            model.add(LeakyReLU(alpha=0.2))
            model.add(BatchNormalization(momentum=0.8))

        model.add(Dense(self.W * self.H * self.C, activation='tanh'))
        model.add(Reshape((self.W, self.H, self.C)))
        
        return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def dc_model(self):

        model = Sequential()

        model.add(Dense(256*8*8,activation=LeakyReLU(0.2), input_dim=self.LATENT_SPACE_SIZE))
        model.add(BatchNormalization())

        model.add(Reshape((8, 8, 256)))
        model.add(UpSampling2D())

        model.add(Convolution2D(128, 5, 5, border_mode='same',activation=LeakyReLU(0.2)))
        model.add(BatchNormalization())
        model.add(UpSampling2D())

        model.add(Convolution2D(64, 5, 5, border_mode='same',activation=LeakyReLU(0.2)))
        model.add(BatchNormalization())
        model.add(UpSampling2D())

        model.add(Convolution2D(self.C, 5, 5, border_mode='same', activation='tanh'))
        
        return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def call(self, inputs):
        def wrapper(rois, mrcnn_class, mrcnn_bbox, image_meta):
            detections_batch = []
            for b in range(self.config.BATCH_SIZE):
                _, _, window, _ = parse_image_meta(image_meta)
                detections = refine_detections(
                    rois[b], mrcnn_class[b], mrcnn_bbox[b], window[b], self.config)
                # Pad with zeros if detections < DETECTION_MAX_INSTANCES
                gap = self.config.DETECTION_MAX_INSTANCES - detections.shape[0]
                assert gap >= 0
                if gap > 0:
                    detections = np.pad(
                        detections, [(0, gap), (0, 0)], 'constant', constant_values=0)
                detections_batch.append(detections)

            # Stack detections and cast to float32
            # TODO: track where float64 is introduced
            detections_batch = np.array(detections_batch).astype(np.float32)
            # Reshape output
            # [batch, num_detections, (y1, x1, y2, x2, class_score)] in pixels
            return np.reshape(detections_batch, [self.config.BATCH_SIZE, self.config.DETECTION_MAX_INSTANCES, 6])

        # Return wrapped function
        return tf.py_func(wrapper, inputs, tf.float32) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def build_discriminator(self):
        """Discriminator network with PatchGAN."""
        inp_img = Input(shape = (self.image_size, self.image_size, 3))
        x = ZeroPadding2D(padding = 1)(inp_img)
        x = Conv2D(filters = self.d_conv_dim, kernel_size = 4, strides = 2, padding = 'valid', use_bias = False)(x)
        x = LeakyReLU(0.01)(x)
    
        curr_dim = self.d_conv_dim
        for i in range(1, self.d_repeat_num):
            x = ZeroPadding2D(padding = 1)(x)
            x = Conv2D(filters = curr_dim*2, kernel_size = 4, strides = 2, padding = 'valid')(x)
            x = LeakyReLU(0.01)(x)
            curr_dim = curr_dim * 2
    
        kernel_size = int(self.image_size / np.power(2, self.d_repeat_num))
    
        out_src = ZeroPadding2D(padding = 1)(x)
        out_src = Conv2D(filters = 1, kernel_size = 3, strides = 1, padding = 'valid', use_bias = False)(out_src)
    
        out_cls = Conv2D(filters = self.c_dim, kernel_size = kernel_size, strides = 1, padding = 'valid', use_bias = False)(x)
        out_cls = Reshape((self.c_dim, ))(out_cls)
    
        return Model(inp_img, [out_src, out_cls]) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def ssr_F_model_build(self, feat_dim, name_F):
        input_s1_pre = Input((feat_dim,))
        input_s2_pre = Input((feat_dim,))
        input_s3_pre = Input((feat_dim,))

        def _process_input(stage_index, stage_num, num_classes, input_s_pre):            
            feat_delta_s = FeatSliceLayer(0,4)(input_s_pre)            
            delta_s = Dense(num_classes,activation='tanh',name=f'delta_s{stage_index}')(feat_delta_s)            
            
            feat_local_s = FeatSliceLayer(4,8)(input_s_pre)            
            local_s = Dense(units=num_classes, activation='tanh', name=f'local_delta_stage{stage_index}')(feat_local_s)
            
            feat_pred_s = FeatSliceLayer(8,16)(input_s_pre)            
            feat_pred_s = Dense(stage_num*num_classes,activation='relu')(feat_pred_s) 
            pred_s = Reshape((num_classes,stage_num))(feat_pred_s)

            return delta_s, local_s, pred_s

        delta_s1, local_s1, pred_s1 = _process_input(1, self.stage_num[0], self.num_classes, input_s1_pre)
        delta_s2, local_s2, pred_s2 = _process_input(2, self.stage_num[1], self.num_classes, input_s2_pre)
        delta_s3, local_s3, pred_s3 = _process_input(3, self.stage_num[2], self.num_classes, input_s3_pre)        
    
        return Model(inputs=[input_s1_pre,input_s2_pre,input_s3_pre],outputs=[pred_s1,pred_s2,pred_s3,delta_s1,delta_s2,delta_s3,local_s1,local_s2,local_s3], name=name_F) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import Reshape [as 别名]
def ssr_FC_model_build(self, feat_dim, name_F):
        input_s1_pre = Input((feat_dim,))
        input_s2_pre = Input((feat_dim,))
        input_s3_pre = Input((feat_dim,))

        def _process_input(stage_index, stage_num, num_classes, input_s_pre):
            feat_delta_s = Dense(2*num_classes,activation='tanh')(input_s_pre)
            delta_s = Dense(num_classes,activation='tanh',name=f'delta_s{stage_index}')(feat_delta_s)

            feat_local_s = Dense(2*num_classes,activation='tanh')(input_s_pre)
            local_s = Dense(units=num_classes, activation='tanh', name=f'local_delta_stage{stage_index}')(feat_local_s)

            feat_pred_s = Dense(stage_num*num_classes,activation='relu')(input_s_pre) 
            pred_s = Reshape((num_classes,stage_num))(feat_pred_s)     

            return delta_s, local_s, pred_s   

        delta_s1, local_s1, pred_s1 = _process_input(1, self.stage_num[0], self.num_classes, input_s1_pre)
        delta_s2, local_s2, pred_s2 = _process_input(2, self.stage_num[1], self.num_classes, input_s2_pre)
        delta_s3, local_s3, pred_s3 = _process_input(3, self.stage_num[2], self.num_classes, input_s3_pre)        
           
        return Model(inputs=[input_s1_pre,input_s2_pre,input_s3_pre],outputs=[pred_s1,pred_s2,pred_s3,delta_s1,delta_s2,delta_s3,local_s1,local_s2,local_s3], name=name_F)