traffic-model3-create-cnn.py

from keras.layers import Flatten, Input, concatenate
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.core import Activation, Dropout, Dense
from keras.layers.normalization import BatchNormalization
from keras.models import Model

def create_cnn(width, height, depth, filters=(16, 32, 64), regularizer=None):
    """
    Creates a CNN with the given input dimension and filter numbers.
    """
    # Initialize the input shape and channel dimension, where the number of channels is the last dimension
    inputShape = (height, width, depth)
    chanDim = -1
 
    # Define the model input
    inputs = Input(shape=inputShape)
 
    # Loop over the number of filters 
    for (i, f) in enumerate(filters):
        # If this is the first CONV layer then set the input appropriately
        if i == 0:
            x = inputs
 
        # Create loops of CONV => RELU => BN => POOL layers
        x = Conv2D(f, (3, 3), padding="same")(x)
        x = Activation("relu")(x)
        x = BatchNormalization(axis=chanDim)(x)
        x = MaxPooling2D(pool_size=(2, 2))(x)
        
    # Final layers - flatten the volume, then Fully-Connected => RELU => BN => DROPOUT
    x = Flatten()(x)
    x = Dense(16, kernel_regularizer=regularizer)(x)
    x = Activation("relu")(x)
    x = BatchNormalization(axis=chanDim)(x)
    x = Dropout(0.5)(x)
 
    # Apply another fully-connected layer, this one to match the number of nodes coming out of the MLP
    x = Dense(4, kernel_regularizer=regularizer)(x)
    x = Activation("relu")(x)
 
    # Construct the CNN
    model = Model(inputs, x)
 
    # Return the CNN
    return model