Revert Dense layer changes that attempt to support 1d inputs.
PiperOrigin-RevId: 296232311
Change-Id: Iffc4ea2267b8846dec15397328ef9bf1ddc21760
diff --git a/tensorflow/python/keras/layers/core.py b/tensorflow/python/keras/layers/core.py
index 65aadd7..32ad7a8 100644
--- a/tensorflow/python/keras/layers/core.py
+++ b/tensorflow/python/keras/layers/core.py
@@ -1122,17 +1122,11 @@
raise TypeError('Unable to build `Dense` layer with non-floating point '
'dtype %s' % (dtype,))
input_shape = tensor_shape.TensorShape(input_shape)
- # Handle 1-d inputs by reshaping to (-1, 1).
- if input_shape.rank == 1:
- input_shape = tensor_shape.TensorShape(input_shape.as_list() + [1])
- last_dim = tensor_shape.dimension_value(1)
- self.input_spec = InputSpec(min_ndim=1, max_ndim=2)
- else:
- if tensor_shape.dimension_value(input_shape[-1]) is None:
- raise ValueError('The last dimension of the inputs to `Dense` '
- 'should be defined. Found `None`.')
- last_dim = tensor_shape.dimension_value(input_shape[-1])
- self.input_spec = InputSpec(min_ndim=2, axes={-1: last_dim})
+ if tensor_shape.dimension_value(input_shape[-1]) is None:
+ raise ValueError('The last dimension of the inputs to `Dense` '
+ 'should be defined. Found `None`.')
+ last_dim = tensor_shape.dimension_value(input_shape[-1])
+ self.input_spec = InputSpec(min_ndim=2, axes={-1: last_dim})
self.kernel = self.add_weight(
'kernel',
shape=[last_dim, self.units],
@@ -1165,8 +1159,6 @@
output_shape = shape[:-1] + [self.units]
outputs.set_shape(output_shape)
else:
- if rank == 1:
- inputs = array_ops.expand_dims_v2(inputs, axis=-1)
inputs = math_ops.cast(inputs, self._compute_dtype)
if K.is_sparse(inputs):
outputs = sparse_ops.sparse_tensor_dense_matmul(inputs, self.kernel)
