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自学教程:Python layers.InputLayer方法代码示例

51自学网 2020-12-01 11:09:15
  Keras
这篇教程Python layers.InputLayer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.InputLayer方法的典型用法代码示例。如果您正苦于以下问题:Python layers.InputLayer方法的具体用法?Python layers.InputLayer怎么用?Python layers.InputLayer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.InputLayer方法的10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: build_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputLayer [as 别名]def build_model():    model = Sequential()    model.add(InputLayer(input_shape=(None, None, 1)))    model.add(Conv2D(8, (3, 3), activation='relu', padding='same', strides=2))    model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))    model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))    model.add(Conv2D(16, (3, 3), activation='relu', padding='same', strides=2))    model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))    model.add(Conv2D(32, (3, 3), activation='relu', padding='same', strides=2))    model.add(UpSampling2D((2, 2)))    model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))    model.add(UpSampling2D((2, 2)))    model.add(Conv2D(16, (3, 3), activation='relu', padding='same'))    model.add(UpSampling2D((2, 2)))    model.add(Conv2D(2, (3, 3), activation='tanh', padding='same'))    # model.compile(optimizer='rmsprop', loss='mse')    model.compile(optimizer='adam', loss='mse')    return model#训练数据 
开发者ID:vipstone,项目名称:faceai,代码行数:23,代码来源:colorize.py


示例2: fsrcnn

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputLayer [as 别名]def fsrcnn(x, d=56, s=12, m=4, scale=3):    """Build an FSRCNN model.    See https://arxiv.org/abs/1608.00367    """    model = Sequential()    model.add(InputLayer(input_shape=x.shape[-3:]))    c = x.shape[-1]    f = [5, 1] + [3] * m + [1]    n = [d, s] + [s] * m + [d]    for ni, fi in zip(n, f):        model.add(Conv2D(ni, fi, padding='same',                         kernel_initializer='he_normal', activation='relu'))    model.add(Conv2DTranspose(c, 9, strides=scale, padding='same',                              kernel_initializer='he_normal'))    return model 
开发者ID:qobilidop,项目名称:srcnn,代码行数:18,代码来源:models.py


示例3: nsfsrcnn

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputLayer [as 别名]def nsfsrcnn(x, d=56, s=12, m=4, scale=3, pos=1):    """Build an FSRCNN model, but change deconv position.    See https://arxiv.org/abs/1608.00367    """    model = Sequential()    model.add(InputLayer(input_shape=x.shape[-3:]))    c = x.shape[-1]    f1 = [5, 1] + [3] * pos    n1 = [d, s] + [s] * pos    f2 = [3] * (m - pos - 1) + [1]    n2 = [s] * (m - pos - 1) + [d]    f3 = 9    n3 = c    for ni, fi in zip(n1, f1):        model.add(Conv2D(ni, fi, padding='same',                         kernel_initializer='he_normal', activation='relu'))    model.add(Conv2DTranspose(s, 3, strides=scale, padding='same',                              kernel_initializer='he_normal'))    for ni, fi in zip(n2, f2):        model.add(Conv2D(ni, fi, padding='same',                         kernel_initializer='he_normal', activation='relu'))    model.add(Conv2D(n3, f3, padding='same',                         kernel_initializer='he_normal'))    return model 
开发者ID:qobilidop,项目名称:srcnn,代码行数:27,代码来源:models.py


示例4: espcn

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputLayer [as 别名]def espcn(x, f=[5, 3, 3], n=[64, 32], scale=3):    """Build an ESPCN model.    See https://arxiv.org/abs/1609.05158    """    assert len(f) == len(n) + 1    model = Sequential()    model.add(InputLayer(input_shape=x.shape[1:]))    c = x.shape[-1]    for ni, fi in zip(n, f):        model.add(Conv2D(ni, fi, padding='same',                         kernel_initializer='he_normal', activation='tanh'))    model.add(Conv2D(c * scale ** 2, f[-1], padding='same',                     kernel_initializer='he_normal'))    model.add(Conv2DSubPixel(scale))    return model 
开发者ID:qobilidop,项目名称:srcnn,代码行数:18,代码来源:models.py


示例5: make_model_small

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputLayer [as 别名]def make_model_small(train_input, num_classes, weights_file=None):    '''Return Cifar10 DL model with small number layers.'''    model = Sequential()    # model.add(KL.InputLayer(input_shape=inshape[1:]))    if isinstance(train_input, tf.Tensor):        model.add(KL.InputLayer(input_tensor=train_input))    else:        model.add(KL.InputLayer(input_shape=train_input))    # if standardize:    #     model.add(KL.Lambda(stand_img))    model.add(KL.Conv2D(32, (3, 3), padding='same'))    model.add(KL.Activation('relu'))    model.add(KL.Flatten())    # model.add(Dropout(0.5))    model.add(KL.Dense(num_classes))    model.add(KL.Activation('softmax'))    if weights_file is not None and os.path.exists(weights_file):        model.load_weights(weights_file)    return model 
开发者ID:avolkov1,项目名称:keras_experiments,代码行数:26,代码来源:cifar_common.py


示例6: compute_output_shape

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputLayer [as 别名]def compute_output_shape(self, input_shape):        return input_shape# class LocalParam(InputLayer):#     def __init__(self, shape, mult=1, my_initializer='RandomNormal', **kwargs):#         super(LocalParam, self).__init__(input_shape=shape, **kwargs)              #         # Create a trainable weight variable for this layer.#         self.kernel = self.add_weight(name='kernel', #                                       shape=tuple(shape),#                                       initializer=my_initializer,#                                       trainable=True)        #         outputs = self._inbound_nodes[0].output_tensors#         z = Input(tensor=K.expand_dims(self.kernel, 0)*mult)#         if len(outputs) == 1:#             self._inbound_nodes[0].output_tensors[0] = z#         else:#             self._inbound_nodes[0].output_tensors = z      #     def get_output(self):  # call() would force inputs#             outputs = self._inbound_nodes[0].output_tensors#             if len(outputs) == 1:#                 return outputs[0]#             else:#                 return outputs 
开发者ID:voxelmorph,项目名称:voxelmorph,代码行数:30,代码来源:layers.py


示例7: bicubic

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputLayer [as 别名]def bicubic(x, scale=3):    model = Sequential()    model.add(InputLayer(input_shape=x.shape[-3:]))    model.add(ImageRescale(scale, method=tf.image.ResizeMethod.BICUBIC))    return model 
开发者ID:qobilidop,项目名称:srcnn,代码行数:7,代码来源:models.py


示例8: make_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputLayer [as 别名]def make_model(inshape, num_classes, weights_file=None):    model = Sequential()    model.add(KL.InputLayer(input_shape=inshape[1:]))    # model.add(KL.Conv2D(32, (3, 3), padding='same', input_shape=inshape[1:]))    model.add(KL.Conv2D(32, (3, 3), padding='same'))    model.add(KL.Activation('relu'))    model.add(KL.Conv2D(32, (3, 3)))    model.add(KL.Activation('relu'))    model.add(KL.MaxPooling2D(pool_size=(2, 2)))    model.add(KL.Dropout(0.25))    model.add(KL.Conv2D(64, (3, 3), padding='same'))    model.add(KL.Activation('relu'))    model.add(KL.Conv2D(64, (3, 3)))    model.add(KL.Activation('relu'))    model.add(KL.MaxPooling2D(pool_size=(2, 2)))    model.add(KL.Dropout(0.25))    model.add(KL.Flatten())    model.add(KL.Dense(512))    model.add(KL.Activation('relu'))    model.add(KL.Dropout(0.5))    model.add(KL.Dense(num_classes))    model.add(KL.Activation('softmax'))    if weights_file is not None and os.path.exists(weights_file):        model.load_weights(weights_file)    return model 
开发者ID:avolkov1,项目名称:keras_experiments,代码行数:31,代码来源:cifar10_cnn_distrib_v2_slurm.py


示例9: make_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputLayer [as 别名]def make_model(x_train_input, nclasses):    '''Non-functional model definition.'''    model = Sequential()    model.add(KL.InputLayer(input_tensor=x_train_input))    ll = cnn_layers_list(nclasses)    for il in ll:        model.add(il)    return model 
开发者ID:avolkov1,项目名称:keras_experiments,代码行数:11,代码来源:mnist_tfrecord_mgpu.py


示例10: make_model_full

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputLayer [as 别名]def make_model_full(train_input, num_classes, weights_file=None):    '''Return Cifar10 DL model with many layers.    :param train_input: Either a tf.Tensor input placeholder/pipeline, or a        tuple input shape.    '''    model = Sequential()    # model.add(KL.InputLayer(input_shape=inshape[1:]))    if isinstance(train_input, tf.Tensor):        model.add(KL.InputLayer(input_tensor=train_input))    else:        model.add(KL.InputLayer(input_shape=train_input))    # if standardize:    #     model.add(KL.Lambda(stand_img))    model.add(KL.Conv2D(32, (3, 3), padding='same'))    model.add(KL.Activation('relu'))    model.add(KL.Conv2D(32, (3, 3)))    model.add(KL.Activation('relu'))    model.add(KL.MaxPooling2D(pool_size=(2, 2)))    model.add(KL.Dropout(0.25))    model.add(KL.Conv2D(64, (3, 3), padding='same'))    model.add(KL.Activation('relu'))    model.add(KL.Conv2D(64, (3, 3)))    model.add(KL.Activation('relu'))    model.add(KL.MaxPooling2D(pool_size=(2, 2)))    model.add(KL.Dropout(0.25))    model.add(KL.Flatten())    model.add(KL.Dense(512))    model.add(KL.Activation('relu'))    model.add(KL.Dropout(0.5))    model.add(KL.Dense(num_classes))    model.add(KL.Activation('softmax'))    if weights_file is not None and os.path.exists(weights_file):        model.load_weights(weights_file)    return model 
开发者ID:avolkov1,项目名称:keras_experiments,代码行数:44,代码来源:cifar_common.py


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