I have a dataset where there are 500 images of dimension (32, 32, 3) and its label which is a single integer. Thus making my label a shape (). However when I run my code, it gives error where:
Shapes () and (1, 32, 32, 3) are incompatible
I'm assuming my model is requiring a (32x32x3) dimension data, so how should I alter my label (integer of 0 to 7) into a (1,32,32,3) shape to fit?
Summary of model:
Code I'm running:
for i in range(20):
n = random.randint(0, len(data_manager.X_test))
label = data_manager.y_test[n]
#label_samples = np.zeros((1,32,32,3), dtype=label.dtype)
img = data_manager.X_test[n]
img = tf.expand_dims(img, axis=0)
img = np.array(img)
img = img.astype('float32')
true_pred = our_network_skip.predict(img)
pa = pgd_attack(our_network_skip, img, label,
epsilon=0.0313,
num_steps=20,
step_size=0.002,
clip_value_min=0.,
clip_value_max=1.0,
soft_label=False,
from_logits= False) #error from this function where label has to be (1,32,32,3)
Function for the error:
def pgd_attack(model, input_image, input_label= None,
epsilon=0.0313,
num_steps=20,
step_size=0.002,
clip_value_min=0.,
clip_value_max=1.0,
soft_label=False,
from_logits= False):
loss_fn = tf.keras.losses.categorical_crossentropy #compute CE loss from logits or prediction probabilities
if type(input_image) is np.ndarray:
input_image = tf.convert_to_tensor(input_image)
if type(input_label) is np.ndarray:
input_label = tf.convert_to_tensor(input_label)
# random initialization around input_image
random_noise = tf.random.uniform(shape=input_image.shape, minval=-epsilon, maxval=epsilon)
adv_image = input_image + random_noise
for _ in range(num_steps):
with tf.GradientTape(watch_accessed_variables=False) as tape:
tape.watch(adv_image)
if not soft_label:
loss = loss_fn(input_label, adv_image, from_logits= from_logits) # use ground-truth label to attack
else:
pred_label = tf.math.argmax(adv_image, axis=1)
loss = loss_fn(pred_label, adv_image, from_logits= from_logits) # use predicted label to attack
gradient = tape.gradient(loss, adv_image) # get the gradient of the loss w.r.t. the current point
adv_image = adv_image + step_size * tf.sign(gradient) # move current adverarial example along the gradient direction with step size is eta
adv_image = tf.clip_by_value(adv_image, input_image-epsilon, input_image+epsilon) # clip to a valid boundary
adv_image = tf.clip_by_value(adv_image, clip_value_min, clip_value_max) # clip to a valid range
adv_image = tf.stop_gradient(adv_image) # stop the gradient to make the adversarial image as a constant input
return adv_image
Full error:
ValueError Traceback (most recent call last)
Input In [57], in <cell line: 48>()
57 img = img.astype('float32')
60 true_pred = our_network_skip.predict(img)
---> 62 pa = pgd_attack(our_network_skip, img, label,
63 epsilon=0.0313,
64 num_steps=20,
65 step_size=0.002,
66 clip_value_min=0.,
67 clip_value_max=1.0,
68 soft_label=False,
69 from_logits= False)
71 pgd_pred = our_network_skip.predict(pa)
72 print("True label: {}, adversarial label: {}".format(true_pred, pgd_pred))
Input In [57], in pgd_attack(model, input_image, input_label, epsilon, num_steps, step_size, clip_value_min, clip_value_max, soft_label, from_logits)
32 tape.watch(adv_image)
34 if not soft_label:
---> 35 loss = loss_fn(input_label, adv_image, from_logits= from_logits) # use ground-truth label to attack
36 else:
37 pred_label = tf.math.argmax(adv_image, axis=1)
File ~\anaconda3\envs\tf2_cpu\lib\site-packages\tensorflow\python\util\dispatch.py:206, in add_dispatch_support.<locals>.wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
209 # TypeError, when given unexpected types. So we need to catch both.
210 result = dispatch(wrapper, args, kwargs)
File ~\anaconda3\envs\tf2_cpu\lib\site-packages\keras\losses.py:1665, in categorical_crossentropy(y_true, y_pred, from_logits, label_smoothing, axis)
1660 return y_true * (1.0 - label_smoothing) + (label_smoothing / num_classes)
1662 y_true = tf.__internal__.smart_cond.smart_cond(label_smoothing, _smooth_labels,
1663 lambda: y_true)
-> 1665 return backend.categorical_crossentropy(
1666 y_true, y_pred, from_logits=from_logits, axis=axis)
File ~\anaconda3\envs\tf2_cpu\lib\site-packages\tensorflow\python\util\dispatch.py:206, in add_dispatch_support.<locals>.wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
209 # TypeError, when given unexpected types. So we need to catch both.
210 result = dispatch(wrapper, args, kwargs)
File ~\anaconda3\envs\tf2_cpu\lib\site-packages\keras\backend.py:4839, in categorical_crossentropy(target, output, from_logits, axis)
4837 target = tf.convert_to_tensor(target)
4838 output = tf.convert_to_tensor(output)
-> 4839 target.shape.assert_is_compatible_with(output.shape)
4841 # Use logits whenever they are available. `softmax` and `sigmoid`
4842 # activations cache logits on the `output` Tensor.
4843 if hasattr(output, '_keras_logits'):
File ~\anaconda3\envs\tf2_cpu\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1161, in TensorShape.assert_is_compatible_with(self, other)
1149 """Raises exception if `self` and `other` do not represent the same shape.
1150
1151 This method can be used to assert that there exists a shape that both
(...)
1158 ValueError: If `self` and `other` do not represent the same shape.
1159 """
1160 if not self.is_compatible_with(other):
-> 1161 raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes () and (1, 32, 32, 3) are incompatible
Thank you!