def helper_test_coupling(my_activation, tf_activation, loss, inputs, y_true,
units):
tf.random.set_seed(42)
tf_layer = Dense(units, activation=tf_activation)
tf_layer.build(inputs.shape)
with tf.GradientTape(persistent=True) as tape:
tape.watch([inputs, *tf_layer.trainable_weights])
pred_tf = tf_layer(inputs)
loss = loss(y_true, pred_tf)
*grads_tf, dY = tape.gradient(
loss, [inputs, *tf_layer.trainable_weights, pred_tf])
tf.random.set_seed(42)
my_layer = NN.Layer(units, my_activation)
my_layer.build(inputs.shape)
pred_my = my_layer(inputs)
dX, [dW, dB] = my_layer.backprop(dY)
grads_my = [dX, dW, dB]
assert np.allclose(pred_my, pred_tf)
assert all(
np.allclose(grad_my, grad_tf)
for grad_my, grad_tf in zip(grads_my, grads_tf))
# %%
X, Y = datasets.make_moons(n_samples=6_000, noise=0.3, random_state=0)
# %%
tf.random.set_seed(42)
loss = losses.BinaryCrossentropy()
dataset = (tf.data.Dataset.from_tensor_slices(
(tf.cast(X, dtype=tf.float32),
tf.cast(Y, dtype=tf.int32))).shuffle(X.shape[0]).batch(64))
clf = NN.NeuralNet()
clf.add(NN.Layer(units=3, activation=activations.ReLU()))
clf.add(NN.Layer(units=3, activation=activations.ReLU()))
clf.add(NN.Layer(units=3, activation=activations.ReLU()))
clf.add(NN.Layer(units=1, activation=activations.Sigmoid()))
train(
NN=clf,
dataset=dataset,
loss=loss,
optimizer=optimization.Adam(),
epochs=50,
use_tape=False,
)
plot_decision(clf, X, Y)
