Python tensorflow.python.keras.layers.MaxPooling2D() Examples

The following are 2 code examples of tensorflow.python.keras.layers.MaxPooling2D(). 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 tensorflow.python.keras.layers , or try the search function .
Example #1
Source Project: camera-trap-classifier   Author: marco-willi   File: small_cnn.py    License: MIT License 5 votes vote down vote up
def architecture(inputs):
    """ Architecture of model """

    conv1 = Conv2D(32, kernel_size=(3, 3),
                   activation='relu')(inputs)
    max1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    conv2 = Conv2D(32, (3, 3), activation='relu')(max1)
    max2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    conv3 = Conv2D(64, (3, 3), activation='relu')(max2)
    max3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    flat1 = Flatten()(max3)
    dense1 = Dense(64, activation='relu')(flat1)
    drop1 = Dropout(0.5)(dense1)

    return drop1 
Example #2
Source Project: ray   Author: ray-project   File: pbt_tune_cifar10_with_keras.py    License: Apache License 2.0 4 votes vote down vote up
def _build_model(self, input_shape):
        x = Input(shape=(32, 32, 3))
        y = x
        y = Convolution2D(
            filters=64,
            kernel_size=3,
            strides=1,
            padding="same",
            activation="relu",
            kernel_initializer="he_normal")(y)
        y = Convolution2D(
            filters=64,
            kernel_size=3,
            strides=1,
            padding="same",
            activation="relu",
            kernel_initializer="he_normal")(y)
        y = MaxPooling2D(pool_size=2, strides=2, padding="same")(y)

        y = Convolution2D(
            filters=128,
            kernel_size=3,
            strides=1,
            padding="same",
            activation="relu",
            kernel_initializer="he_normal")(y)
        y = Convolution2D(
            filters=128,
            kernel_size=3,
            strides=1,
            padding="same",
            activation="relu",
            kernel_initializer="he_normal")(y)
        y = MaxPooling2D(pool_size=2, strides=2, padding="same")(y)

        y = Convolution2D(
            filters=256,
            kernel_size=3,
            strides=1,
            padding="same",
            activation="relu",
            kernel_initializer="he_normal")(y)
        y = Convolution2D(
            filters=256,
            kernel_size=3,
            strides=1,
            padding="same",
            activation="relu",
            kernel_initializer="he_normal")(y)
        y = MaxPooling2D(pool_size=2, strides=2, padding="same")(y)

        y = Flatten()(y)
        y = Dropout(self.config.get("dropout", 0.5))(y)
        y = Dense(
            units=10, activation="softmax", kernel_initializer="he_normal")(y)

        model = Model(inputs=x, outputs=y, name="model1")
        return model