m1_tf_test.py

@@ -30,9 +30,9 @@ class ComputeSum(Layer):
         n = len(data)
         return n
 
-def ComputeSumModel(input_shape):
-    inputs = Input(shape = input_shape)
-    outputs = ComputeSum(input_shape[0])(inputs)
+  def ComputeSumModel(input_shape):
+      inputs = Input(shape = input_shape)
+      outputs = ComputeSum(input_shape[0])(inputs)
     
     model = tf.keras.Model(inputs = inputs, outputs = outputs)
 
@@ -91,7 +91,7 @@ loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 model.compile(optimizer='adam',
  loss=loss_fn,
  metrics=['accuracy'])
-these = model.fit(x_train, y_train, epochs=10)
+these = model.fit(x_train, y_train, epochs=10).history
 that = ComputeSum(len(these))
 those = that(these)
 outputs = tf.keras.layers.Dense(4, activation='softmax', name='predictions')(those)