CNN.py

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout

# Create training and testing datasets from tensors
train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train)).batch(1)
test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(1)

# CNN Model
class Command(Model):
    def __init__(self):
        super(Command,self).__init__()
        self.conv1= Conv2D(32,3,padding='same',activation='relu')
        self.poo11 = MaxPooling2D((2,2))
        self.conv2= Conv2D(64,3,padding='same',activation='relu')
        self.pool2 = MaxPooling2D((2,2))
        self.conv3= Conv2D(128,3,padding='same',activation='relu')
        self.flatten = Flatten()
        self.fc1=Dense(256, activation='relu')
        self.fc2=Dropout(0.4)
        self.fc3=Dense(100, activation='relu')
        self.fc4=Dropout(0.4)
        self.fc5=Dense(5, activation='softmax')
    def call(self,x):
        x=self.conv1(x)
        x=self.pool1(x)
        x=self.conv2(x)
        x=self.pool2(x)
        x=self.conv3(x)
        x=self.flatten(x)
        x=self.fc1(x)
        x=self.fc2(x)
        x=self.fc3(x)
        x=self.fc4(x)
        x=self.fc5(x)
        return x