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[TF2] spsa #1158

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[TF2] spsa #1158

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10 changes: 7 additions & 3 deletions cleverhans/future/tf2/attacks/spsa.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,11 @@ def spsa(model_fn, x, y, eps, nb_iter, clip_min=None, clip_max=None, targeted=Fa
is on `spsa_samples` different inputs.
:param is_debug: If True, print the adversarial loss after each update.
"""
if x.get_shape().as_list()[0] != 1:

if tf.is_tensor(x) is False:
x = tf.convert_to_tensor(x)

if tf.shape(x)[0] != 1:
raise ValueError("For SPSA, input tensor x must have batch_size of 1.")

optimizer = SPSAAdam(lr=learning_rate, delta=delta, num_samples=spsa_samples,
Expand All @@ -44,7 +48,7 @@ def loss_fn(x, label):
"""
Margin logit loss, with correct sign for targeted vs untargeted loss.
"""
logits = model_fn(x)
logits = model_fn(x, training=False)
loss_multiplier = 1 if targeted else -1
return loss_multiplier * margin_logit_loss(logits, label, nb_classes=logits.get_shape()[-1])

Expand Down Expand Up @@ -108,7 +112,7 @@ def body(i, grad_array):
loss_vals = tf.reshape(
loss_fn(x + delta_x),
[2 * self._num_samples] + [1] * (len(x_shape) - 1))
avg_grad = tf.reduce_mean(loss_vals * delta_x, axis=0) / delta
avg_grad = tf.reduce_mean(loss_vals / delta_x, axis=0)
avg_grad = tf.expand_dims(avg_grad, axis=0)
new_grad_array = grad_array.write(i, avg_grad)
return i + 1, new_grad_array
Expand Down