Replace Input with InputLayer everywhere for the sake of consistency

ViktorC · Jan 7, 2023 · 59a5b18 · 59a5b18
1 parent b2a9a5c
commit 59a5b18
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Showing 6 changed files with 87 additions and 51 deletions.
diff --git a/examples/diffusion_1d_pidon.py b/examples/diffusion_1d_pidon.py
@@ -41,11 +41,11 @@
     ),
     model_args=ModelArgs(
         branch_net=tf.keras.Sequential(
-            [tf.keras.layers.Input(np.prod(cp.y_vertices_shape).item())]
+            [tf.keras.layers.InputLayer(np.prod(cp.y_vertices_shape).item())]
             + [tf.keras.layers.Dense(50, activation="tanh") for _ in range(8)]
         ),
         trunk_net=tf.keras.Sequential(
-            [tf.keras.layers.Input(diff_eq.x_dimension + 1)]
+            [tf.keras.layers.InputLayer(diff_eq.x_dimension + 1)]
             + [tf.keras.layers.Dense(50, activation="tanh") for _ in range(8)]
         ),
         combiner_net=tf.keras.Sequential(

diff --git a/examples/lotka_volterra_pidon.py b/examples/lotka_volterra_pidon.py
@@ -43,11 +43,11 @@
     ),
     model_args=ModelArgs(
         branch_net=tf.keras.Sequential(
-            [tf.keras.layers.Input(np.prod(cp.y_vertices_shape).item())]
+            [tf.keras.layers.InputLayer(np.prod(cp.y_vertices_shape).item())]
             + [tf.keras.layers.Dense(100, activation="tanh") for _ in range(6)]
         ),
         trunk_net=tf.keras.Sequential(
-            [tf.keras.layers.Input(diff_eq.x_dimension + 1)]
+            [tf.keras.layers.InputLayer(diff_eq.x_dimension + 1)]
             + [tf.keras.layers.Dense(100, activation="tanh") for _ in range(6)]
         ),
         combiner_net=tf.keras.Sequential(

diff --git a/examples/population_growth_pidon.py b/examples/population_growth_pidon.py
@@ -31,11 +31,11 @@
     ),
     model_args=ModelArgs(
         branch_net=tf.keras.Sequential(
-            [tf.keras.layers.Input(np.prod(cp.y_vertices_shape).item())]
+            [tf.keras.layers.InputLayer(np.prod(cp.y_vertices_shape).item())]
             + [tf.keras.layers.Dense(100, activation="tanh") for _ in range(6)]
         ),
         trunk_net=tf.keras.Sequential(
-            [tf.keras.layers.Input(diff_eq.x_dimension + 1)]
+            [tf.keras.layers.InputLayer(diff_eq.x_dimension + 1)]
             + [tf.keras.layers.Dense(100, activation="tanh") for _ in range(6)]
         ),
         combiner_net=tf.keras.Sequential(

diff --git a/tests/operators/ml/auto_regression/test_auto_regression_operator.py b/tests/operators/ml/auto_regression/test_auto_regression_operator.py
@@ -210,23 +210,25 @@ def build_model():
         model = DeepONet(
             branch_net=tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(np.prod(cp.y_shape(True)).item()),
+                    tf.keras.layers.InputLayer(
+                        np.prod(cp.y_shape(True)).item()
+                    ),
                     tf.keras.layers.Dense(100, activation="tanh"),
                     tf.keras.layers.Dense(50, activation="tanh"),
                     tf.keras.layers.Dense(diff_eq.y_dimension * 10),
                 ]
             ),
             trunk_net=tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(diff_eq.x_dimension),
+                    tf.keras.layers.InputLayer(diff_eq.x_dimension),
                     tf.keras.layers.Dense(50, activation="tanh"),
                     tf.keras.layers.Dense(50, activation="tanh"),
                     tf.keras.layers.Dense(diff_eq.y_dimension * 10),
                 ]
             ),
             combiner_net=tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(3 * diff_eq.y_dimension * 10),
+                    tf.keras.layers.InputLayer(3 * diff_eq.y_dimension * 10),
                     tf.keras.layers.Dense(
                         diff_eq.y_dimension,
                         kernel_regularizer=tf.keras.regularizers.L2(l2=1e-5),

diff --git a/tests/operators/ml/pidon/test_pi_deeponet.py b/tests/operators/ml/pidon/test_pi_deeponet.py
@@ -16,19 +16,19 @@ def test_pi_deeponet_with_mismatched_branch_and_trunk_net_output_shapes():
         PIDeepONet(
             tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(1),
+                    tf.keras.layers.InputLayer(1),
                     tf.keras.layers.Dense(5),
                 ]
             ),
             tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(1),
+                    tf.keras.layers.InputLayer(1),
                     tf.keras.layers.Dense(3),
                 ]
             ),
             tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(15),
+                    tf.keras.layers.InputLayer(15),
                     tf.keras.layers.Dense(1),
                 ]
             ),
@@ -43,19 +43,19 @@ def test_pi_deeponet_with_wrong_combiner_net_output_shape():
         PIDeepONet(
             tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(1),
+                    tf.keras.layers.InputLayer(1),
                     tf.keras.layers.Dense(5),
                 ]
             ),
             tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(1),
+                    tf.keras.layers.InputLayer(1),
                     tf.keras.layers.Dense(5),
                 ]
             ),
             tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(15),
+                    tf.keras.layers.InputLayer(15),
                     tf.keras.layers.Dense(2),
                 ]
             ),
@@ -70,19 +70,19 @@ def test_pi_deeponet_with_wrong_loss_weight_length():
         PIDeepONet(
             tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(3),
+                    tf.keras.layers.InputLayer(3),
                     tf.keras.layers.Dense(3),
                 ]
             ),
             tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(1),
+                    tf.keras.layers.InputLayer(1),
                     tf.keras.layers.Dense(3),
                 ]
             ),
             tf.keras.Sequential(
                 [
-                    tf.keras.layers.Input(9),
+                    tf.keras.layers.InputLayer(9),
                     tf.keras.layers.Dense(3),
                 ]
             ),
@@ -96,19 +96,19 @@ def test_pi_deeponet_loss_weight_broadcasting():
     pidon = PIDeepONet(
         tf.keras.Sequential(
             [
-                tf.keras.layers.Input(3),
+                tf.keras.layers.InputLayer(3),
                 tf.keras.layers.Dense(3),
             ]
         ),
         tf.keras.Sequential(
             [
-                tf.keras.layers.Input(1),
+                tf.keras.layers.InputLayer(1),
                 tf.keras.layers.Dense(3),
             ]
         ),
         tf.keras.Sequential(
             [
-                tf.keras.layers.Input(9),
+                tf.keras.layers.InputLayer(9),
                 tf.keras.layers.Dense(3),
             ]
         ),