Max Epochs = 10 ADAM sparse_categorical_crossentropy import tensorflow as tf fashion_mnist = tf.keras.datasets.fashion_mnist class myCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if(logs.get('acc')>0.99): print("\nReached 99% accuracy so cancelling training!") self.model.stop_training = True mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = fashion_mnist.load_data() x_train = x_train/255.0 x_test = x_test/255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(100, activation=tf.nn.relu), tf.keras.layers.Dense(10, activation=tf.nn.softmax) ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accur...