Merge pull request #94 from keisen/issues/invalid-argument-type-for-c…

…allbacks

Illegal argument type for callbacks

CITATION.cff

@@ -8,8 +8,8 @@ title: "tf-keras-vis"
 repository: "https://github.com/keisen/tf-keras-vis"
 url: "https://keisen.github.io/tf-keras-vis-docs/"
 type: software
-version: 0.8.3
-date-released: "2022-11-24"
+version: 0.8.4
+date-released: "2022-11-25"
 license-url: "https://github.com/keisen/tf-keras-vis/blob/master/LICENSE"
 references:
   - authors:

VERSION

@@ -1 +1 @@
-0.8.3
+0.8.4

tf_keras_vis/activation_maximization/__init__.py

@@ -433,10 +433,10 @@ def _clip_and_modify(self, seed_inputs, input_ranges, activation_modifiers):
         input_ranges = [(input_tensor.dtype.min if low is None else low,
                          input_tensor.dtype.max if high is None else high)
                         for (low, high), input_tensor in zip(input_ranges, self.model.inputs)]
-        clipped_values = (np.array(K.clip(X, low, high))
+        clipped_values = (K.clip(X, low, high)
                           for X, (low, high) in zip(seed_inputs, input_ranges))
-        clipped_values = (X.astype(np.int) if isinstance(t, int) else X.astype(np.float32)
-                          for X, (t, _) in zip(clipped_values, input_ranges))
+        clipped_values = (tf.cast(X, input_tensor.dtype)
+                          for X, input_tensor in zip(clipped_values, self.model.inputs))
         if activation_modifiers is not None:
             clipped_values = ((activation_modifiers[name], seed_input)
                               for name, seed_input in zip(self.model.input_names, clipped_values))

tf_keras_vis/activation_maximization/callbacks.py

@@ -98,7 +98,8 @@ def __call__(self, i, values, *args, **kwargs) -> None:
         if self.data is None:
             self.data = [[] for _ in range(len(values))]
         for n, value in enumerate(values):
-            img = Image.fromarray(value[0].astype(np.uint8))  # 1st image in the batch
+            value = value[0].numpy() if tf.is_tensor(value[0]) else value[0]
+            img = Image.fromarray(value.astype(np.uint8))  # 1st image in the batch
             ImageDraw.Draw(img).text((10, 10), f"Step {i + 1}", font=ImageFont.load_default())
             self.data[n].append(np.asarray(img))
 

tf_keras_vis/saliency.py

@@ -80,7 +80,7 @@ def __call__(self,
         # Processing saliency
         if smooth_samples > 0:
             smooth_samples = get_num_of_steps_allowed(smooth_samples)
-            seed_inputs = (tf.tile(X, (smooth_samples, ) + tuple(np.ones(X.ndim - 1, np.int)))
+            seed_inputs = (tf.tile(X, (smooth_samples, ) + tuple(np.ones(X.ndim - 1, np.int32)))
                            for X in seed_inputs)
             seed_inputs = (tf.reshape(X, (smooth_samples, -1) + tuple(X.shape[1:]))
                            for X in seed_inputs)