python类MaxPooling2D()的实例源码

flowers_gridsearch.py 文件源码 项目:cv_ml 作者: techfort 项目源码 文件源码 阅读 33 收藏 0 点赞 0 评论 0
def create_model(learning_rate=0.1, momentum=0.9):
    model = Sequential()
    model.add(Convolution2D(20, 9, 9, border_mode='same', input_shape=(3, SIZE, SIZE)))
    model.add(Activation("relu"))
    model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
    model.add(Convolution2D(50, 5, 5, activation = "relu"))
    model.add(Activation("relu"))
    model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
    model.add(Flatten())
    model.add(Dense(768, input_dim=3072, init='uniform', activation = 'relu'))
    model.add(Dropout(0.1))
    model.add(Dense(384, init = 'uniform',  activation = 'relu', W_constraint=maxnorm(3)))
    model.add(Dense(4))
    model.add(Activation("softmax"))
    sgd = SGD(lr=learning_rate, momentum=momentum, nesterov=True, decay=1e-6)
    model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=["accuracy"])
    return model
gan.py 文件源码 项目:qtim_ROP 作者: QTIM-Lab 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def discriminator_model():
    model = Sequential()
    model.add(Convolution2D(
                        64, 5, 5,
                        border_mode='same',
                        input_shape=(1, 28, 28)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(128, 5, 5))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(1024))
    model.add(Activation('tanh'))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))
    return model
Nerve.py 文件源码 项目:Nerve-Segmentation 作者: matthewzhou 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def create_model(img_rows, img_cols):
    model = Sequential() #initialize model
    model.add(Convolution2D(4, 3, 3, border_mode='same', activation='relu', init='he_normal',
                            input_shape=(1, img_rows, img_cols)))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Convolution2D(8, 3, 3, border_mode='same', activation='relu', init='he_normal'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(2))
    model.add(Activation('softmax'))
    adm = Adamax()
    #sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(optimizer=adm, loss='categorical_crossentropy')
    return model
dcgan.py 文件源码 项目:deeplearning_keras 作者: gazzola 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def discriminator_model():
    model = Sequential()
    model.add(Convolution2D(
                        64, 5, 5,
                        border_mode='same',
                        input_shape=(1, 28, 28)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(128, 5, 5))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(1024))
    model.add(Activation('tanh'))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))
    return model
keras_LeNet.py 文件源码 项目:deeplearning_keras 作者: gazzola 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def build(input_shape, classes):
        model = Sequential()
        # CONV => RELU => POOL
        model.add(Conv2D(20, kernel_size=5, padding="same",
            input_shape=input_shape))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        # CONV => RELU => POOL
        model.add(Conv2D(50, kernel_size=5, padding="same"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
        # Flatten => RELU layers
        model.add(Flatten())
        model.add(Dense(500))
        model.add(Activation("relu"))

        # a softmax classifier
        model.add(Dense(classes))
        model.add(Activation("softmax"))

        return model

# network and training
test.py 文件源码 项目:DL8803 作者: NanditaDamaraju 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def VGG_16(weights_path=None):
    model = Sequential()
    model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))
    print "convolution"
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(Flatten())
    print"FLATTEN"
    model.add(Dense(400, activation='relu'))
    model.add(Dropout(0.5))
    print"YO"
    model.add(Dense(10, activation='softmax'))

    return model
inception_resnet_v2.py 文件源码 项目:Inception-v4 作者: titu1994 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def reduction_A(input, k=192, l=224, m=256, n=384):
    if K.image_dim_ordering() == "th":
        channel_axis = 1
    else:
        channel_axis = -1

    r1 = MaxPooling2D((3,3), strides=(2,2))(input)

    r2 = Convolution2D(n, 3, 3, activation='relu', subsample=(2,2))(input)

    r3 = Convolution2D(k, 1, 1, activation='relu', border_mode='same')(input)
    r3 = Convolution2D(l, 3, 3, activation='relu', border_mode='same')(r3)
    r3 = Convolution2D(m, 3, 3, activation='relu', subsample=(2,2))(r3)

    m = merge([r1, r2, r3], mode='concat', concat_axis=channel_axis)
    m = BatchNormalization(axis=1)(m)
    m = Activation('relu')(m)
    return m
inception_resnet_v2.py 文件源码 项目:Inception-v4 作者: titu1994 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def reduction_resnet_v2_B(input):
    if K.image_dim_ordering() == "th":
        channel_axis = 1
    else:
        channel_axis = -1

    r1 = MaxPooling2D((3,3), strides=(2,2), border_mode='valid')(input)

    r2 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
    r2 = Convolution2D(384, 3, 3, activation='relu', subsample=(2,2))(r2)

    r3 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
    r3 = Convolution2D(288, 3, 3, activation='relu', subsample=(2, 2))(r3)

    r4 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
    r4 = Convolution2D(288, 3, 3, activation='relu', border_mode='same')(r4)
    r4 = Convolution2D(320, 3, 3, activation='relu', subsample=(2, 2))(r4)

    m = merge([r1, r2, r3, r4], concat_axis=channel_axis, mode='concat')
    m = BatchNormalization(axis=channel_axis)(m)
    m = Activation('relu')(m)
    return m
inception_v4.py 文件源码 项目:Inception-v4 作者: titu1994 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def reduction_A(input):
    if K.image_dim_ordering() == "th":
        channel_axis = 1
    else:
        channel_axis = -1

    r1 = conv_block(input, 384, 3, 3, subsample=(2, 2), border_mode='valid')

    r2 = conv_block(input, 192, 1, 1)
    r2 = conv_block(r2, 224, 3, 3)
    r2 = conv_block(r2, 256, 3, 3, subsample=(2, 2), border_mode='valid')

    r3 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(input)

    m = merge([r1, r2, r3], mode='concat', concat_axis=channel_axis)
    return m
inception_resnet_v1.py 文件源码 项目:Inception-v4 作者: titu1994 项目源码 文件源码 阅读 17 收藏 0 点赞 0 评论 0
def inception_resnet_stem(input):
    if K.image_dim_ordering() == "th":
        channel_axis = 1
    else:
        channel_axis = -1

    # Input Shape is 299 x 299 x 3 (tf) or 3 x 299 x 299 (th)
    c = Convolution2D(32, 3, 3, activation='relu', subsample=(2, 2))(input)
    c = Convolution2D(32, 3, 3, activation='relu', )(c)
    c = Convolution2D(64, 3, 3, activation='relu', )(c)
    c = MaxPooling2D((3, 3), strides=(2, 2))(c)
    c = Convolution2D(80, 1, 1, activation='relu', border_mode='same')(c)
    c = Convolution2D(192, 3, 3, activation='relu')(c)
    c = Convolution2D(256, 3, 3, activation='relu', subsample=(2,2), border_mode='same')(c)
    b = BatchNormalization(axis=channel_axis)(c)
    b = Activation('relu')(b)
    return b
inception_resnet_v1.py 文件源码 项目:Inception-v4 作者: titu1994 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def reduction_A(input, k=192, l=224, m=256, n=384):
    if K.image_dim_ordering() == "th":
        channel_axis = 1
    else:
        channel_axis = -1

    r1 = MaxPooling2D((3,3), strides=(2,2))(input)

    r2 = Convolution2D(n, 3, 3, activation='relu', subsample=(2,2))(input)

    r3 = Convolution2D(k, 1, 1, activation='relu', border_mode='same')(input)
    r3 = Convolution2D(l, 3, 3, activation='relu', border_mode='same')(r3)
    r3 = Convolution2D(m, 3, 3, activation='relu', subsample=(2,2))(r3)

    m = merge([r1, r2, r3], mode='concat', concat_axis=channel_axis)
    m = BatchNormalization(axis=channel_axis)(m)
    m = Activation('relu')(m)
    return m
inception_resnet_v1.py 文件源码 项目:Inception-v4 作者: titu1994 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def reduction_resnet_B(input):
    if K.image_dim_ordering() == "th":
        channel_axis = 1
    else:
        channel_axis = -1

    r1 = MaxPooling2D((3,3), strides=(2,2), border_mode='valid')(input)

    r2 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
    r2 = Convolution2D(384, 3, 3, activation='relu', subsample=(2,2))(r2)

    r3 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
    r3 = Convolution2D(256, 3, 3, activation='relu', subsample=(2, 2))(r3)

    r4 = Convolution2D(256, 1, 1, activation='relu', border_mode='same')(input)
    r4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(r4)
    r4 = Convolution2D(256, 3, 3, activation='relu', subsample=(2, 2))(r4)

    m = merge([r1, r2, r3, r4], concat_axis=channel_axis, mode='concat')
    m = BatchNormalization(axis=channel_axis)(m)
    m = Activation('relu')(m)
    return m
dcgans_amc.py 文件源码 项目:jamespy_py3 作者: jskDr 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def discriminator_model():
    model = Sequential()
    model.add(Convolution2D(
        64, 5, 5,
        border_mode='same',
        input_shape=(1, 28, 28)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(128, 5, 5))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(1024))
    model.add(Activation('tanh'))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))
    return model
lsgans.py 文件源码 项目:jamespy_py3 作者: jskDr 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def discriminator_model():
    model = Sequential()
    model.add(Convolution2D(
        64, 5, 5,
        border_mode='same',
        input_shape=(1, 28, 28)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(128, 5, 5))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(1024))
    model.add(Activation('tanh'))
    model.add(Dense(1))
    # model.add(Activation('sigmoid'))  # LSGANs
    return model
aegans.py 文件源码 项目:jamespy_py3 作者: jskDr 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def generator_model_r1():  # CDNN Model
    model = Sequential()
    model.add(Convolution2D(
        1, 5, 5,
        border_mode='same',
        input_shape=(1, 14, 14)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Convolution2D(64, 5, 5, border_mode='same'))
    model.add(Activation('tanh'))

    model.add(UpSampling2D(size=(2, 2)))
    model.add(Convolution2D(64, 5, 5, border_mode='same'))
    model.add(Activation('tanh'))
    model.add(UpSampling2D(size=(2, 2)))
    model.add(Convolution2D(1, 5, 5, border_mode='same'))
    model.add(Activation('tanh'))
    return model
aegans.py 文件源码 项目:jamespy_py3 作者: jskDr 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def discriminator_model():
    model = Sequential()
    model.add(Convolution2D(
        64, 5, 5,
        border_mode='same',
        input_shape=(1, 28, 28)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(128, 5, 5))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(1024))
    model.add(Activation('tanh'))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))
    return model
gans4sr.py 文件源码 项目:jamespy_py3 作者: jskDr 项目源码 文件源码 阅读 69 收藏 0 点赞 0 评论 0
def discriminator_model():
    model = Sequential()
    model.add(Convolution2D(
        64, 5, 5,
        border_mode='same',
        input_shape=(1, 28, 28)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(128, 5, 5))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(1024))
    model.add(Activation('tanh'))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))
    return model
aegans_recon.py 文件源码 项目:jamespy_py3 作者: jskDr 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def discriminator_model():
    model = Sequential()
    model.add(Convolution2D(
        64, 5, 5,
        border_mode='same',
        input_shape=(1, 28, 28)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Convolution2D(128, 5, 5))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(1024))
    model.add(Activation('tanh'))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))
    return model
aegans_recon.py 文件源码 项目:jamespy_py3 作者: jskDr 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def discriminator_model(self):
        model = Sequential()
        model.add(Convolution2D(
            8, 10, 10,
            border_mode='same',
            input_shape=(1, 144, 144)))
        model.add(Activation('tanh'))
        model.add(MaxPooling2D(pool_size=(4, 4)))
        model.add(Convolution2D(16, 10, 10))
        model.add(Activation('tanh'))
        model.add(MaxPooling2D(pool_size=(4, 4)))
        model.add(Flatten())
        model.add(Dense(128))
        model.add(Activation('tanh'))
        model.add(Dense(1))
        model.add(Activation('sigmoid'))
        return model
aegans_recon.py 文件源码 项目:jamespy_py3 作者: jskDr 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def discriminator_model(self):
        model = Sequential()
        model.add(Convolution2D(
            8, 10, 10,
            border_mode='same',
            input_shape=(1, 144, 144)))
        model.add(Activation('tanh'))
        model.add(MaxPooling2D(pool_size=(4, 4)))
        model.add(Convolution2D(16, 10, 10))
        model.add(Activation('tanh'))
        model.add(MaxPooling2D(pool_size=(4, 4)))
        model.add(Flatten())
        model.add(Dense(128))
        model.add(Activation('tanh'))
        model.add(Dense(1))
        model.add(Activation('sigmoid'))
        return model


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