Update m1_tf_test.py

m1_tf_test.py

@@ -10,24 +10,31 @@ import tensorflow as tf
 tf.__version__
 tf.config.list_physical_devices()
 logits = [[4.0, 2.0, 1.0], [0.0, 5.0, 1.0]]
+inputs = tf.keras.Input(shape=(784,), name="digits")
 model = tf.keras.models.load_model('model')
 mnist = tf.keras.datasets.mnist
 (x_train, y_train), (x_test, y_test) = mnist.load_data()
 x_train, x_test = x_train / 255.0, x_test / 255.0
 model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
- tf.keras.layers.Dense(128,activation='selu'),
+ tf.keras.layers.Dense(128,activation='selu',name='layer1'),
  tf.keras.layers.Dropout(0.2),
- tf.keras.layers.Dense(10,activation='tanh'),
+ tf.keras.layers.Dense(64,activation='relu',name='layer2'),
  tf.keras.layers.Dropout(0.2),
- tf.keras.layers.Dense(2,activation='sigmoid'),
+ tf.keras.layers.Dense(32,activation='elu',name='layer3'),
+ tf.keras.layers.Dropout(0.2),
+ tf.keras.layers.Dense(16,activation='tanh',name='layer4'),
+ tf.keras.layers.Dropout(0.2),
+ tf.keras.layers.Dense(8,activation='sigmoid',name='layer5'),
  tf.keras.layers.Dropout(0.2)
 ])
 loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 model.compile(optimizer='adam',
  loss=loss_fn,
  metrics=['accuracy'])
-model.fit(x_train, y_train, epochs=10)
+model.fit(x_test, y_test, epochs=10)
+outputs = tf.keras.layers.Dense(4, activation="softmax", name="predictions")(x_test)
+model = tf.keras.Model(inputs=inputs, outputs=outputs)
 model.build()
 model.save('model')
 model.summary()