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Try to fix CI failure #790

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merged 3 commits into from
Mar 28, 2024
Merged

Try to fix CI failure #790

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e7d9403
Try to fix CI failure
MatthewDaggitt Mar 26, 2024
e289a8b
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Mar 26, 2024
0d42e07
Fixed dimension mismatch
MatthewDaggitt Mar 28, 2024
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1 change: 1 addition & 0 deletions CONTRIBUTING.md
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Original file line number Diff line number Diff line change
Expand Up @@ -748,6 +748,7 @@ python -m pip install -e .[test]
```

This installs the Python bindings in [editable mode], which directly adds the files in the development directory are added to Python's import path.
Note that you must have the preferred version of GHC (see above) active in order to run this command otherwise you'll get a `Error: cabal: Could not resolve dependencies` error message.

When the Python bindings are installed in editable mode, you can run pytest directly:

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38 changes: 20 additions & 18 deletions vehicle-python/tests/test_lossdl2_exec_tf_bounded.py
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Expand Up @@ -45,8 +45,8 @@ def optimiser_for_x(

X_train = np.array([[0.0], [0.2], [0.4], [0.6], [0.8]])
X_test = np.array([[0.1], [0.3], [0.5], [0.7], [0.9]])
y_train = np.array([0, 0, 0, 1, 1])
y_test = np.array([0, 0, 1, 1, 1])
y_train = np.array([[0], [0], [0], [1], [1]])
y_test = np.array([[0], [0], [1], [1], [1]])

train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
test_dataset = tf.data.Dataset.from_tensor_slices((X_test, y_test))
Expand All @@ -57,13 +57,11 @@ def optimiser_for_x(
# Train the network
num_epochs = 4

optimizer = tf.keras.optimizers.legacy.Adam()
optimizer = tf.keras.optimizers.Adam()
ce_batch_loss = tf.keras.losses.BinaryCrossentropy()

train_acc_metric = tf.keras.metrics.BinaryCrossentropy()
test_acc_metric = tf.keras.metrics.BinaryCrossentropy()
train_loss_metric = tf.keras.metrics.BinaryCrossentropy()
test_loss_metric = tf.keras.metrics.BinaryCrossentropy()

ce_loss_weight = 0
bounded_weight = 1
Expand Down Expand Up @@ -94,24 +92,28 @@ def optimiser_for_x(
train_acc_metric.update_state(y_batch_train, train_outputs)
train_loss_metric.update_state(y_batch_train, train_outputs)

# Run a testing loop at the end of each epoch.
for x_batch_test, y_batch_test in test_dataset:
test_outputs = model(x_batch_test, training=False)
test_acc_metric.update_state(y_batch_test, test_outputs)
test_loss_metric.update_state(y_batch_test, test_outputs)

train_acc = train_acc_metric.result()
test_acc = test_acc_metric.result()
train_loss = train_loss_metric.result()
test_loss = test_loss_metric.result()

train_acc_metric.reset_states()
test_acc_metric.reset_states()
train_loss_metric.reset_states()
test_loss_metric.reset_states()
train_acc_metric.reset_state()
train_loss_metric.reset_state()

print(f"Train acc: {float(train_acc):.4f}")
print(f"Train loss: {float(train_loss):.4f}")

test_acc_metric = tf.keras.metrics.BinaryCrossentropy()
test_loss_metric = tf.keras.metrics.BinaryCrossentropy()

test_loss = test_loss_metric.result()
test_acc = test_acc_metric.result()

# Run a testing loop at the end of each epoch.
for x_batch_test, y_batch_test in test_dataset:
test_outputs = model(x_batch_test, training=False)
test_acc_metric.update_state(y_batch_test, test_outputs)
test_loss_metric.update_state(y_batch_test, test_outputs)

test_acc_metric.reset_state()
test_loss_metric.reset_state()
print(f"Test acc: {float(test_acc):.4f}")
print(f"Test loss: {float(test_loss):.4f}")

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