From 2d17cffaa650e80d4f6888c7bbcea3eac2f18c5a Mon Sep 17 00:00:00 2001
From: solegalli <solegalli@protonmail.com>
Date: Sun, 26 Jan 2025 17:44:09 -0300
Subject: [PATCH 1/3] replaced input output in keras
---
eli5/keras/explain_prediction.py | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/eli5/keras/explain_prediction.py b/eli5/keras/explain_prediction.py
index 73deb25b..7f64379b 100644
--- a/eli5/keras/explain_prediction.py
+++ b/eli5/keras/explain_prediction.py
@@ -53,7 +53,7 @@ def explain_prediction_keras(model, # type: Model
The tensor must be of suitable shape for the ``model``.
- Check ``model.input_shape`` to confirm the required dimensions of the input tensor.
+ Check ``model.input.shape`` to confirm the required dimensions of the input tensor.
:raises TypeError: if ``doc`` is not a numpy array.
@@ -260,7 +260,7 @@ def _validate_doc(model, doc):
"""
if not isinstance(doc, np.ndarray):
raise TypeError('doc must be a numpy.ndarray, got: {}'.format(doc))
- input_sh = model.input_shape
+ input_sh = model.input.shape
doc_sh = doc.shape
if len(input_sh) == 4:
# rank 4 with (batch, ...) shape
@@ -337,6 +337,6 @@ def _is_suitable_activation_layer(model, layer):
# check layer name
# a check that asks "can we resize this activation layer over the image?"
- rank = len(layer.output_shape)
- required_rank = len(model.input_shape)
+ rank = len(layer.output.shape)
+ required_rank = len(model.input.shape)
return rank == required_rank
From ebc6f737f9761ce398cd61a4ed4a3c214d1ae733 Mon Sep 17 00:00:00 2001
From: solegalli <solegalli@protonmail.com>
Date: Sun, 26 Jan 2025 22:07:46 -0300
Subject: [PATCH 2/3] refactor simple sequential
---
tests/test_keras.py | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/tests/test_keras.py b/tests/test_keras.py
index e4631175..278c0e54 100644
--- a/tests/test_keras.py
+++ b/tests/test_keras.py
@@ -38,12 +38,14 @@
def simple_seq():
"""A simple sequential model for images."""
model = Sequential([
- Activation('linear', input_shape=(32, 32, 1)), # index 0, input
+ Input((32, 32, 1)),
+ Activation('linear'), # index 0, input
conv_layer, # index 1, conv
Conv2D(20, (3, 3)), # index 2, conv2
GlobalAveragePooling2D(), # index 3, gap
# output shape is (None, 20)
])
+ model(Input((32, 32, 1)))
print('Summary of model:')
model.summary()
# rename layers
From a3149510d8a9e9f4071eee7bfd711576cacafed5 Mon Sep 17 00:00:00 2001
From: solegalli <solegalli@protonmail.com>
Date: Mon, 27 Jan 2025 15:16:45 -0300
Subject: [PATCH 3/3] fix keras
---
tests/test_keras.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/tests/test_keras.py b/tests/test_keras.py
index 278c0e54..893b5792 100644
--- a/tests/test_keras.py
+++ b/tests/test_keras.py
@@ -103,7 +103,7 @@ def test_validate_doc(simple_seq):
def test_validate_doc_custom():
# model with custom (not rank 4) input shape
- model = Sequential([Dense(1, input_shape=(2, 3))])
+ model = Sequential(Input((2, 3)), [Dense(1)])
# not matching shape
with pytest.raises(ValueError):
_validate_doc(model, np.zeros((5, 3)))