Fix all conv layers when filters is 0

tensorflow/python/keras/layers/convolutional.py

@@ -168,6 +168,10 @@ def _validate_init(self):
           'The number of filters must be evenly divisible by the number of '
           'groups. Received: groups={}, filters={}'.format(
               self.groups, self.filters))
+
+    if self.filters == 0:
+      raise ValueError('The value of the `filters` argument should not '
+                       'be zero')
 
     if not all(self.kernel_size):
       raise ValueError('The argument `kernel_size` cannot contain 0(s). '

tensorflow/python/keras/layers/convolutional_test.py

@@ -166,6 +166,12 @@ def fn(inpt):
       fn(inpt2)
       self.assertEqual(outp1_shape, layer(inpt1).shape)
 
+  def test_conv1d_zero_filters(self):
+    kwargs = {'filters': 0, 'kernel_size': 2}
+    with self.assertRaisesRegex(ValueError, 'The value of the `filters` '
+                                'argument should not be zero'):
+      keras.layers.Conv1D(**kwargs)  
+
 
 @keras_parameterized.run_all_keras_modes
 class Conv2DTest(keras_parameterized.TestCase):
@@ -298,6 +304,12 @@ def test_conv2d_zero_kernel_size(self):
     with self.assertRaises(ValueError):
       keras.layers.Conv2D(**kwargs)
 
+  def test_conv2d_zero_filters(self):
+    kwargs = {'filters': 0, 'kernel_size': 2}
+    with self.assertRaisesRegex(ValueError, 'The value of the `filters` '
+                                'argument should not be zero'):
+      keras.layers.Conv2D(**kwargs)
+
 
 @keras_parameterized.run_all_keras_modes
 class Conv3DTest(keras_parameterized.TestCase):
@@ -433,6 +445,12 @@ def test_conv3d_dynamic_shape(self):
             input_shape=(None, 3, None, None, None),
             input_data=input_data)
 
+  def test_conv3d_zero_filters(self):
+    kwargs = {'filters': 0, 'kernel_size': 2}
+    with self.assertRaisesRegex(ValueError, 'The value of the `filters` '
+                                'argument should not be zero'):
+      keras.layers.Conv3D(**kwargs)
+
 
 @keras_parameterized.run_all_keras_modes(always_skip_v1=True)
 class GroupedConvTest(keras_parameterized.TestCase):
@@ -518,6 +536,12 @@ def test_conv1d_transpose(self, kwargs, expected_output_shape=None):
         test.is_gpu_available(cuda_only=True)):
       self._run_test(kwargs, expected_output_shape)
 
+  def test_conv1d_transpose_zero_filters(self):
+    kwargs = {'filters': 0, 'kernel_size': 2}
+    with self.assertRaisesRegex(ValueError, 'The value of the `filters` '
+                                'argument should not be zero'):
+      keras.layers.Conv1DTranspose(**kwargs)
+
 
 @keras_parameterized.run_all_keras_modes
 class Conv3DTransposeTest(keras_parameterized.TestCase):
@@ -551,6 +575,12 @@ def test_conv3d_transpose(self, kwargs, expected_output_shape=None):
     if 'data_format' not in kwargs or test.is_gpu_available(cuda_only=True):
       self._run_test(kwargs, expected_output_shape)
 
+  def test_conv3d_transpose_zero_filters(self):
+    kwargs = {'filters': 0, 'kernel_size': 2}
+    with self.assertRaisesRegex(ValueError, 'The value of the `filters` '
+                                'argument should not be zero'):
+      keras.layers.Conv3DTranspose(**kwargs)
+
 
 @keras_parameterized.run_all_keras_modes
 class ConvSequentialTest(keras_parameterized.TestCase):