fully_connected_layers.py

# Uncomment the below line if you're using dense layers
# x = tf.keras.layers.GlobalMaxPooling2D()(x)

# Fully connected layer 1
# x = tf.keras.layers.Dropout(dropout_rate)(x)
# x = tf.keras.layers.BatchNormalization()(x)
# x = tf.keras.layers.Dense(units=64)(x)
# x = tf.keras.layers.Activation('relu')(x)

# Fully connected layer 1
x = tf.keras.layers.Conv2D(filters=64, kernel_size=1, strides=1)(x)
x = tf.keras.layers.Dropout(dropout_rate)(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Activation('relu')(x)

# Fully connected layer 2
# x = tf.keras.layers.Dropout(dropout_rate)(x)
# x = tf.keras.layers.BatchNormalization()(x)
# x = tf.keras.layers.Dense(units=len_classes)(x)
# predictions = tf.keras.layers.Activation('softmax')(x)

# Fully connected layer 2
x = tf.keras.layers.Conv2D(filters=len_classes, kernel_size=1, strides=1)(x)
x = tf.keras.layers.Dropout(dropout_rate)(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.GlobalMaxPooling2D()(x)
predictions = tf.keras.layers.Activation('softmax')(x)

model = tf.keras.Model(inputs=input, outputs=predictions)
print(model.summary())