Python keras.layers.InputLayer() Examples

The following are 10 code examples of keras.layers.InputLayer(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.layers , or try the search function .
Example #1
Source File: colorize.py    From faceai with MIT License 7 votes vote down vote up 
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
Example #2
Source File: models.py    From srcnn with MIT License 6 votes vote down vote up 
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
Example #3
Source File: models.py    From srcnn with MIT License 6 votes vote down vote up 
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
Example #4
Source File: models.py    From srcnn with MIT License 6 votes vote down vote up 
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
Example #5
Source File: cifar_common.py    From keras_experiments with The Unlicense 6 votes vote down vote up 
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
Example #6
Source File: layers.py    From voxelmorph with GNU General Public License v3.0 5 votes vote down vote up 
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
Example #7
Source File: models.py    From srcnn with MIT License 5 votes vote down vote up 
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
Example #8
Source File: cifar10_cnn_distrib_v2_slurm.py    From keras_experiments with The Unlicense 5 votes vote down vote up 
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
Example #9
Source File: mnist_tfrecord_mgpu.py    From keras_experiments with The Unlicense 5 votes vote down vote up 
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
Example #10
Source File: cifar_common.py    From keras_experiments with The Unlicense 4 votes vote down vote up 
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