Python layers.MaxPooling2D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 MaxPooling2D [as 别名]
def build_cae_model(height=32, width=32, channel=3):
    """
    build convolutional autoencoder model
    """
    input_img = Input(shape=(height, width, channel))

    # encoder
    net = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
    net = MaxPooling2D((2, 2), padding='same')(net)
    net = Conv2D(8, (3, 3), activation='relu', padding='same')(net)
    net = MaxPooling2D((2, 2), padding='same')(net)
    net = Conv2D(4, (3, 3), activation='relu', padding='same')(net)
    encoded = MaxPooling2D((2, 2), padding='same', name='enc')(net)

    # decoder
    net = Conv2D(4, (3, 3), activation='relu', padding='same')(encoded)
    net = UpSampling2D((2, 2))(net)
    net = Conv2D(8, (3, 3), activation='relu', padding='same')(net)
    net = UpSampling2D((2, 2))(net)
    net = Conv2D(16, (3, 3), activation='relu', padding='same')(net)
    net = UpSampling2D((2, 2))(net)
    decoded = Conv2D(channel, (3, 3), activation='sigmoid', padding='same')(net)

    return Model(input_img, decoded) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def build_model(x_train, num_classes):
        # Reset default graph. Keras leaves old ops in the graph,
        # which are ignored for execution but clutter graph
        # visualization in TensorBoard.
        tf.reset_default_graph()

        inputs = KL.Input(shape=x_train.shape[1:], name="input_image")
        x = KL.Conv2D(32, (3, 3), activation='relu', padding="same",
                      name="conv1")(inputs)
        x = KL.Conv2D(64, (3, 3), activation='relu', padding="same",
                      name="conv2")(x)
        x = KL.MaxPooling2D(pool_size=(2, 2), name="pool1")(x)
        x = KL.Flatten(name="flat1")(x)
        x = KL.Dense(128, activation='relu', name="dense1")(x)
        x = KL.Dense(num_classes, activation='softmax', name="dense2")(x)

        return KM.Model(inputs, x, "digit_classifier_model")

    # Load MNIST Data 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def max_pool2d(h_kernel_size, h_stride):

    def compile_fn(di, dh):
        layer = layers.MaxPooling2D(pool_size=dh['kernel_size'],
                                    strides=(dh['stride'], dh['stride']),
                                    padding='same')

        def fn(di):
            return {'out': layer(di['in'])}

        return fn

    return siso_keras_module('MaxPool2D', compile_fn, {
        'kernel_size': h_kernel_size,
        'stride': h_stride,
    }) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def modelF():
    model = Sequential()

    model.add(Convolution2D(32, 3, 3,
                            border_mode='valid',
                            input_shape=(FLAGS.IMAGE_ROWS,
                                         FLAGS.IMAGE_COLS,
                                         FLAGS.NUM_CHANNELS)))
    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Convolution2D(64, 3, 3))
    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())
    model.add(Dense(1024))
    model.add(Activation('relu'))

    model.add(Dense(FLAGS.NUM_CLASSES))

    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def _initial_conv_block_inception(input, initial_conv_filters, weight_decay=5e-4):
    ''' Adds an initial conv block, with batch norm and relu for the DPN
    Args:
        input: input tensor
        initial_conv_filters: number of filters for initial conv block
        weight_decay: weight decay factor
    Returns: a keras tensor
    '''
    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1

    x = Conv2D(initial_conv_filters, (7, 7), padding='same', use_bias=False, kernel_initializer='he_normal',
               kernel_regularizer=l2(weight_decay), strides=(2, 2))(input)
    x = BatchNormalization(axis=channel_axis)(x)
    x = Activation('relu')(x)

    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)

    return x 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def cnn_2D(self, input_shape, modual=''):
        #建立Sequential模型    
        model_in = Input(input_shape) 
        model = Conv2D(
                filters = 6,
                kernel_size = (3, 3),
                input_shape = input_shape,
                activation='relu',
                kernel_initializer='he_normal',
                name = modual+'conv1'
            )(model_in)# now 30x30x6
        model = MaxPooling2D(pool_size=(2,2))(model)# now 15x15x6
        model = Conv2D(
                filters = 8,
                kernel_size = (4, 4),
                activation='relu',
                kernel_initializer='he_normal',
                name = modual+'conv2'
            )(model)# now 12x12x8
        model = MaxPooling2D(pool_size=(2,2))(model)# now 6x6x8
        model = Flatten()(model)
        model = Dropout(0.5)(model)
        model_out = Dense(100, activation='relu', name = modual+'fc1')(model)
      
        return model_in, model_out 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [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 MaxPooling2D [as 别名]
def build_model(self):
        self.model = Sequential()
        self.model.add(Conv2D(32, kernel_size=(3, 3),
                         activation='relu', input_shape=(28, 28, 1)))
        self.model.add(Conv2D(64, (3, 3), activation='relu'))
        self.model.add(MaxPooling2D(pool_size=(2, 2)))
        self.model.add(Dropout(0.25))
        self.model.add(Flatten())
        self.model.add(Dense(128, activation='relu'))
        self.model.add(Dropout(0.5))
        self.model.add(Dense(10, activation='softmax'))

        self.model.compile(
              loss='sparse_categorical_crossentropy',
              optimizer=self.config.model.optimizer,
              metrics=['accuracy']) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def VGG_16():
    '''Model definition'''

    model = Sequential()
    model.add(Conv2D(64, (11, 11,), padding='valid', strides=(4,4), input_shape=(img_height,img_width,num_channels), name='conv1'))
    model.add(Activation('relu', name='relu1'))
    model.add(LocalResponseNormalization(name='norm1'))
    model.add(MaxPooling2D((2,2), padding='same', name='pool1'))

    model.add(Conv2D(256, (5,5), padding='same', name='conv2'))
    model.add(Activation('relu', name='relu2'))
    model.add(LocalResponseNormalization(name='norm2'))
    model.add(MaxPooling2D((2,2), padding='same', name='pool2'))

    model.add(Conv2D(256, (3, 3), padding='same', name='conv3'))
    model.add(Activation('relu', name='relu3'))
    model.add(Conv2D(256, (3, 3), padding='same', name='conv4'))
    model.add(Activation('relu', name='relu4'))
    model.add(Conv2D(256, (3, 3), padding='same', name='conv5'))
    model.add(Activation('relu', name='relu5'))
    model.add(MaxPooling2D((2,2), padding='same', name='pool5'))

    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def get_model():
    model = models.Sequential()
    model.add(layers.Conv2D(16,(3,3),activation='relu',input_shape=(135,240,3),padding = 'same'))
    model.add(layers.MaxPooling2D((2,2)))
    model.add(layers.Conv2D(32,(3,3),activation='relu',padding = 'same'))
    model.add(layers.MaxPooling2D((2,2)))
    model.add(layers.Conv2D(64,(3,3),activation='relu',padding = 'same'))
    model.add(layers.MaxPooling2D((2,2)))
    model.add(layers.Conv2D(64,(3,3),activation='relu',padding = 'same'))
    model.add(layers.MaxPooling2D((2,2)))
    model.add(layers.Conv2D(128,(3,3),activation='relu',padding = 'same'))
    model.add(layers.MaxPooling2D((2,2)))
    model.add(layers.Flatten())
    model.add(layers.Dropout(0.5))
    model.add(layers.Dense(128,activation="relu"))
    model.add(layers.Dropout(0.5))
    model.add(layers.Dense(27,activation="softmax"))

    return model

#model.summary()
#plot_model(model, to_file='model.png') 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def create_vgglike_network(input_shape, weights):
    input = Input(shape=input_shape)

    # input: 192x256 images with 3 channels -> (192, 256, 3) tensors.
    # this applies 32 convolution filters of size 3x3 each.
    x = Conv2D(32, (3, 3), activation='relu')(input)
    x = Conv2D(32, (3, 3), activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)
    x = Dropout(0.25)(x)

    x = Conv2D(64, (3, 3), activation='relu')(x)
    x = Conv2D(64, (3, 3), activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)
    x = Dropout(0.25)(x)

    x = Flatten()(x)
    x = Dense(256, activation='relu')(x)
    x = Dropout(0.5)(x)
    # x = Dense(2, activation='softmax')(x)
    x = Dense(128, activation='relu')(x)

    return Model(input, x) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def build(width, height, depth, total_classes, Saved_Weights_Path=None):
        # Initialize the Model
        model = Sequential()

        # First CONV => RELU => POOL Layer
        model.add(Conv2D(20, 5, 5, border_mode="same", input_shape=(depth, height, width)))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering="th"))

        # Second CONV => RELU => POOL Layer
        model.add(Conv2D(50, 5, 5, border_mode="same"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering="th"))

        # Third CONV => RELU => POOL Layer
        # Convolution -> ReLU Activation Function -> Pooling Layer
        model.add(Conv2D(100, 5, 5, border_mode="same"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), dim_ordering="th"))

        # FC => RELU layers
        #  Fully Connected Layer -> ReLU Activation Function
        model.add(Flatten())
        model.add(Dense(500))
        model.add(Activation("relu"))

        # Using Softmax Classifier for Linear Classification
        model.add(Dense(total_classes))
        model.add(Activation("softmax"))

        # If the saved_weights file is already present i.e model is pre-trained, load that weights
        if Saved_Weights_Path is not None:
            model.load_weights(Saved_Weights_Path)
        return model
# --------------------------------- EOC ------------------------------------ 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def load_model():

    from keras.models import Model
    from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D
    
    tensor_in = Input((60, 200, 3))
    out = tensor_in
    out = Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(out)
    out = Conv2D(filters=32, kernel_size=(3, 3), activation='relu')(out)
    out = MaxPooling2D(pool_size=(2, 2))(out)
    out = Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')(out)
    out = Conv2D(filters=64, kernel_size=(3, 3), activation='relu')(out)
    out = MaxPooling2D(pool_size=(2, 2))(out)
    out = Conv2D(filters=128, kernel_size=(3, 3), padding='same', activation='relu')(out)
    out = Conv2D(filters=128, kernel_size=(3, 3), activation='relu')(out)
    out = MaxPooling2D(pool_size=(2, 2))(out)
    out = Conv2D(filters=256, kernel_size=(3, 3), activation='relu')(out)
    out = MaxPooling2D(pool_size=(2, 2))(out)
    out = Flatten()(out)
    out = Dropout(0.5)(out)
    out = [Dense(37, name='digit1', activation='softmax')(out),\
        Dense(37, name='digit2', activation='softmax')(out),\
        Dense(37, name='digit3', activation='softmax')(out),\
        Dense(37, name='digit4', activation='softmax')(out),\
        Dense(37, name='digit5', activation='softmax')(out),\
        Dense(37, name='digit6', activation='softmax')(out)]
    
    model = Model(inputs=tensor_in, outputs=out)
    
    # Define the optimizer
    model.compile(loss='categorical_crossentropy', optimizer='Adamax', metrics=['accuracy'])
    if 'Windows' in platform.platform():
        model.load_weights('{}\\cnn_weight\\verificatioin_code.h5'.format(PATH)) 
    else:
        model.load_weights('{}/cnn_weight/verificatioin_code.h5'.format(PATH)) 
    
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def build_model(n_classes):

    if K.image_dim_ordering() == 'th':
        input_shape = (1, N_MEL_BANDS, SEGMENT_DUR)
        channel_axis = 1
    else:
        input_shape = (N_MEL_BANDS, SEGMENT_DUR, 1)
        channel_axis = 3
    melgram_input = Input(shape=input_shape)

    m_sizes = [50, 70]
    n_sizes = [1, 3, 5]
    n_filters = [128, 64, 32]
    maxpool_const = 4

    layers = list()

    for m_i in m_sizes:
        for i, n_i in enumerate(n_sizes):
            x = Convolution2D(n_filters[i], m_i, n_i,
                              border_mode='same',
                              init='he_normal',
                              W_regularizer=l2(1e-5),
                              name=str(n_i)+'_'+str(m_i)+'_'+'conv')(melgram_input)
            x = BatchNormalization(axis=channel_axis, mode=0, name=str(n_i)+'_'+str(m_i)+'_'+'bn')(x)
            x = ELU()(x)
            x = MaxPooling2D(pool_size=(N_MEL_BANDS, SEGMENT_DUR/maxpool_const), name=str(n_i)+'_'+str(m_i)+'_'+'pool')(x)
            x = Flatten(name=str(n_i)+'_'+str(m_i)+'_'+'flatten')(x)
            layers.append(x)

    x = merge(layers, mode='concat', concat_axis=channel_axis)
    x = Dropout(0.5)(x)
    x = Dense(n_classes, init='he_normal', W_regularizer=l2(1e-5), activation='softmax', name='prediction')(x)
    model = Model(melgram_input, x)

    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling2D [as 别名]
def resnet_graph(input_image, architecture, stage5=False, train_bn=True):
    """Build a ResNet graph.
        architecture: Can be resnet50 or resnet101
        stage5: Boolean. If False, stage5 of the network is not created
        train_bn: Boolean. Train or freeze Batch Norm layers
    """
    assert architecture in ["resnet50", "resnet101"]
    # Stage 1
    x = KL.ZeroPadding2D((3, 3))(input_image)
    x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x)
    x = BatchNorm(name='bn_conv1')(x, training=train_bn)
    x = KL.Activation('relu')(x)
    C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
    # Stage 2
    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), train_bn=train_bn)
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', train_bn=train_bn)
    C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', train_bn=train_bn)
    # Stage 3
    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', train_bn=train_bn)
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', train_bn=train_bn)
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', train_bn=train_bn)
    C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', train_bn=train_bn)
    # Stage 4
    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', train_bn=train_bn)
    block_count = {"resnet50": 5, "resnet101": 22}[architecture]
    for i in range(block_count):
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i), train_bn=train_bn)
    C4 = x
    # Stage 5
    if stage5:
        x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', train_bn=train_bn)
        x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', train_bn=train_bn)
        C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', train_bn=train_bn)
    else:
        C5 = None
    return [C1, C2, C3, C4, C5]


############################################################
#  Proposal Layer
############################################################