from keras.models import Model
from keras.layers import Conv2D, Dense, Dropout, Flatten, BatchNormalization, Input

def TLDModel(inputShape = (120, 60, 3), numClasses=3):
    
    inputLayer = Input(shape=inputShape)

    x = Conv2D(12, kernel_size= (5,5), strides=(2,2), padding="valid", activation="relu", name="TLD_Conv1")(inputLayer)
    x = Conv2D(24, kernel_size= (3,3), strides=(2,2), padding="valid", activation="relu", name="TLD_Conv2")(x)
    x = Conv2D(48, kernel_size= (3,3), strides=(2,2), padding="valid", activation="relu", name="TLD_Conv3")(x)

    x = Flatten(name="TLD_Flatten")(x)
    
    x = Dense(512, activation="relu", name="TLD_Dense1")(x)
    x = Dense(128, activation="relu", name="TLD_Dense2")(x)
    x = Dense(64, activation="relu", name="TLD_Dense3")(x)
    output = Dense(numClasses, activation="softmax", name="TLD_Output")(x)

    model = Model(inputs=inputLayer, outputs=output)
    model.compile(loss="categorical_crossentropy", optimizer="adam")

    return model

if __name__ == "__main__":
    model = TLDModel()
    model.summary()

85132332a9fc6fa7b3b9d126917ed60aab63d9d0
<mask token>

<mask token>


def TLDModel(inputShape=(120, 60, 3), numClasses=3):
    inputLayer = Input(shape=inputShape)
    x = Conv2D(12, kernel_size=(5, 5), strides=(2, 2), padding='valid',
        activation='relu', name='TLD_Conv1')(inputLayer)
    x = Conv2D(24, kernel_size=(3, 3), strides=(2, 2), padding='valid',
        activation='relu', name='TLD_Conv2')(x)
    x = Conv2D(48, kernel_size=(3, 3), strides=(2, 2), padding='valid',
        activation='relu', name='TLD_Conv3')(x)
    x = Flatten(name='TLD_Flatten')(x)
    x = Dense(512, activation='relu', name='TLD_Dense1')(x)
    x = Dense(128, activation='relu', name='TLD_Dense2')(x)
    x = Dense(64, activation='relu', name='TLD_Dense3')(x)
    output = Dense(numClasses, activation='softmax', name='TLD_Output')(x)
    model = Model(inputs=inputLayer, outputs=output)
    model.compile(loss='categorical_crossentropy', optimizer='adam')
    return model


<mask token>

<mask token>


def TLDModel(inputShape=(120, 60, 3), numClasses=3):
    inputLayer = Input(shape=inputShape)
    x = Conv2D(12, kernel_size=(5, 5), strides=(2, 2), padding='valid',
        activation='relu', name='TLD_Conv1')(inputLayer)
    x = Conv2D(24, kernel_size=(3, 3), strides=(2, 2), padding='valid',
        activation='relu', name='TLD_Conv2')(x)
    x = Conv2D(48, kernel_size=(3, 3), strides=(2, 2), padding='valid',
        activation='relu', name='TLD_Conv3')(x)
    x = Flatten(name='TLD_Flatten')(x)
    x = Dense(512, activation='relu', name='TLD_Dense1')(x)
    x = Dense(128, activation='relu', name='TLD_Dense2')(x)
    x = Dense(64, activation='relu', name='TLD_Dense3')(x)
    output = Dense(numClasses, activation='softmax', name='TLD_Output')(x)
    model = Model(inputs=inputLayer, outputs=output)
    model.compile(loss='categorical_crossentropy', optimizer='adam')
    return model


if __name__ == '__main__':
    model = TLDModel()
    model.summary()

from keras.models import Model
from keras.layers import Conv2D, Dense, Dropout, Flatten, BatchNormalization, Input


def TLDModel(inputShape=(120, 60, 3), numClasses=3):
    inputLayer = Input(shape=inputShape)
    x = Conv2D(12, kernel_size=(5, 5), strides=(2, 2), padding='valid',
        activation='relu', name='TLD_Conv1')(inputLayer)
    x = Conv2D(24, kernel_size=(3, 3), strides=(2, 2), padding='valid',
        activation='relu', name='TLD_Conv2')(x)
    x = Conv2D(48, kernel_size=(3, 3), strides=(2, 2), padding='valid',
        activation='relu', name='TLD_Conv3')(x)
    x = Flatten(name='TLD_Flatten')(x)
    x = Dense(512, activation='relu', name='TLD_Dense1')(x)
    x = Dense(128, activation='relu', name='TLD_Dense2')(x)
    x = Dense(64, activation='relu', name='TLD_Dense3')(x)
    output = Dense(numClasses, activation='softmax', name='TLD_Output')(x)
    model = Model(inputs=inputLayer, outputs=output)
    model.compile(loss='categorical_crossentropy', optimizer='adam')
    return model


if __name__ == '__main__':
    model = TLDModel()
    model.summary()

from keras.models import Model
from keras.layers import Conv2D, Dense, Dropout, Flatten, BatchNormalization, Input

def TLDModel(inputShape = (120, 60, 3), numClasses=3):
    
    inputLayer = Input(shape=inputShape)

    x = Conv2D(12, kernel_size= (5,5), strides=(2,2), padding="valid", activation="relu", name="TLD_Conv1")(inputLayer)
    x = Conv2D(24, kernel_size= (3,3), strides=(2,2), padding="valid", activation="relu", name="TLD_Conv2")(x)
    x = Conv2D(48, kernel_size= (3,3), strides=(2,2), padding="valid", activation="relu", name="TLD_Conv3")(x)

    x = Flatten(name="TLD_Flatten")(x)
    
    x = Dense(512, activation="relu", name="TLD_Dense1")(x)
    x = Dense(128, activation="relu", name="TLD_Dense2")(x)
    x = Dense(64, activation="relu", name="TLD_Dense3")(x)
    output = Dense(numClasses, activation="softmax", name="TLD_Output")(x)

    model = Model(inputs=inputLayer, outputs=output)
    model.compile(loss="categorical_crossentropy", optimizer="adam")

    return model

if __name__ == "__main__":
    model = TLDModel()
    model.summary()
