Record model.compile options

Suppose we have a model of two hidden layers as follows:

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model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),                                     tf.keras.layers.Dense(128, activation=tf.nn.relu),                                     tf.keras.layers.Dense(10, activation=tf.nn.softmax)])

Then our model.compile might have the following as arguments:

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model.compile(optimizer = tf.optimizers.Adam(),               loss = 'sparse_categorical_crossentropy',               metrics=['accuracy'])

With a callback that stop training at desired loss:

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import tensorflow as tf print(tf.__version__)   class myCallback(tf.keras.callbacks.Callback):   def on_epoch_end(self, epoch, logs={}):     if(logs.get('loss')<0.4):       print("\nReached 60% accuracy so cancelling training!")       self.model.stop_training = True   callbacks = myCallback() mnist = tf.keras.datasets.fashion_mnist (training_images, training_labels), (test_images, test_labels) = mnist.load_data() training_images=training_images/255.0 test_images=test_images/255.0 model = tf.keras.models.Sequential([   tf.keras.layers.Flatten(),   tf.keras.layers.Dense(512, activation=tf.nn.relu),   tf.keras.layers.Dense(10, activation=tf.nn.softmax) ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy') model.fit(training_images, training_labels, epochs=5, callbacks=[callbacks])