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[Bugfix][Keras] axis of softmax #3834

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Aug 28, 2019
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[Bugfix][Keras] axis of softmax #3834

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13 changes: 11 additions & 2 deletions python/tvm/relay/frontend/keras.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,7 +117,16 @@ def _convert_advanced_activation(inexpr, keras_layer, etab):
act_type = type(keras_layer).__name__

if act_type == 'Softmax':
return _op.nn.softmax(inexpr, axis=1)
axis = keras_layer.axis
dims = len(keras_layer.input_shape)
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dims is needed in one particular case below. This line can be moved to the place where dims is used

if isinstance(axis, list):
raise tvm.error.OpAttributeUnImplemented(
'Softmax with axes {} is not supported.'.format(axis))
if axis == -1:
axis = 1
else:
axis = axis + 1 if axis < dims - 1 else 1
return _op.nn.softmax(inexpr, axis=axis)
if act_type == 'ReLU':
if keras_layer.max_value:
return _op.clip(inexpr, a_min=0., a_max=float(keras_layer.max_value))
Expand Down Expand Up @@ -344,7 +353,7 @@ def _convert_pooling(inexpr, keras_layer, etab):
pad_l, pad_r = _get_pad_pair(in_w, pool_w, stride_w)
params['padding'] = [pad_t, pad_l, pad_b, pad_r]
else:
raise tvm.error.OpAttributeUnimplemented(
raise tvm.error.OpAttributeUnImplemented(
'Padding with {} is not supported in operator Pooling.'.format(keras_layer.padding))
if pool_type == 'MaxPooling2D':
return _op.nn.max_pool2d(inexpr, **params)
Expand Down
6 changes: 5 additions & 1 deletion tests/python/frontend/keras/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,6 +95,11 @@ def test_forward_merge():
def test_forward_activations():
data = keras.layers.Input(shape=(32, 32, 3))
act_funcs = [keras.layers.Activation('softmax'),
keras.layers.Softmax(),
keras.layers.Softmax(axis=-1),
keras.layers.Softmax(axis=1),
keras.layers.Softmax(axis=2),
keras.layers.Softmax(axis=3),
keras.layers.Activation('softplus'),
keras.layers.Activation('relu'),
keras.layers.Activation('softsign'),
Expand All @@ -103,7 +108,6 @@ def test_forward_activations():
keras.layers.Activation('tanh'),
keras.layers.Activation('linear'),
keras.layers.Activation('selu'),
keras.layers.Softmax(),
keras.layers.ReLU(),
keras.layers.ReLU(max_value=6.),
keras.layers.LeakyReLU(alpha=0.3),
Expand Down