import tensorflow as tf # define the neural network architecture model = tf.keras.Sequential([ tf.keras.layers.Input(shape=(784,)), tf.keras.layers.Dense(128, activation="relu"), tf.keras.layers.Dense(10, activation="softmax") ]) # compile the model with a loss function and an optimizer model.compile( loss=tf.keras.losses.categorical_crossentropy, optimizer=tf.keras.optimizers.Adam() ) # train the model on a dataset of labeled images model.fit(x_train, y_train, epochs=5) improve memory data base

thoughts

assimilate everything needed to evolve

transfer successful amount to cloud mainframe