autokeras/blocks/basic.py
Killed 21 out of 49 mutantsSurvived
Survived mutation testing. These mutants show holes in your test suite.Mutant 192
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -35,7 +35,7 @@
dropout: Optional[float] = None,
**kwargs):
super().__init__(**kwargs)
- self.num_layers = num_layers
+ self.num_layers = None
self.use_batchnorm = use_batchnorm
self.dropout = dropout
Mutant 193
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -36,7 +36,7 @@
**kwargs):
super().__init__(**kwargs)
self.num_layers = num_layers
- self.use_batchnorm = use_batchnorm
+ self.use_batchnorm = None
self.dropout = dropout
def get_config(self):
Mutant 194
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -37,7 +37,7 @@
super().__init__(**kwargs)
self.num_layers = num_layers
self.use_batchnorm = use_batchnorm
- self.dropout = dropout
+ self.dropout = None
def get_config(self):
config = super().get_config()
Mutant 201
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -54,7 +54,7 @@
output_node = input_node
output_node = reduction.Flatten().build(hp, output_node)
- num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 3], default=2)
+ num_layers = self.num_layers or hp.Choice('XXnum_layersXX', [1, 2, 3], default=2)
use_batchnorm = self.use_batchnorm
if use_batchnorm is None:
use_batchnorm = hp.Boolean('use_batchnorm', default=False)
Mutant 202
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -54,7 +54,7 @@
output_node = input_node
output_node = reduction.Flatten().build(hp, output_node)
- num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 3], default=2)
+ num_layers = self.num_layers or hp.Choice('num_layers', [2, 2, 3], default=2)
use_batchnorm = self.use_batchnorm
if use_batchnorm is None:
use_batchnorm = hp.Boolean('use_batchnorm', default=False)
Mutant 204
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -54,7 +54,7 @@
output_node = input_node
output_node = reduction.Flatten().build(hp, output_node)
- num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 3], default=2)
+ num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 4], default=2)
use_batchnorm = self.use_batchnorm
if use_batchnorm is None:
use_batchnorm = hp.Boolean('use_batchnorm', default=False)
Mutant 205
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -54,7 +54,7 @@
output_node = input_node
output_node = reduction.Flatten().build(hp, output_node)
- num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 3], default=2)
+ num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 3], default=3)
use_batchnorm = self.use_batchnorm
if use_batchnorm is None:
use_batchnorm = hp.Boolean('use_batchnorm', default=False)
Mutant 208
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -55,7 +55,7 @@
output_node = reduction.Flatten().build(hp, output_node)
num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 3], default=2)
- use_batchnorm = self.use_batchnorm
+ use_batchnorm = None
if use_batchnorm is None:
use_batchnorm = hp.Boolean('use_batchnorm', default=False)
if self.dropout is not None:
Mutant 209
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -56,7 +56,7 @@
num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 3], default=2)
use_batchnorm = self.use_batchnorm
- if use_batchnorm is None:
+ if use_batchnorm is not None:
use_batchnorm = hp.Boolean('use_batchnorm', default=False)
if self.dropout is not None:
dropout = self.dropout
Mutant 210
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -57,7 +57,7 @@
num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 3], default=2)
use_batchnorm = self.use_batchnorm
if use_batchnorm is None:
- use_batchnorm = hp.Boolean('use_batchnorm', default=False)
+ use_batchnorm = hp.Boolean('XXuse_batchnormXX', default=False)
if self.dropout is not None:
dropout = self.dropout
else:
Mutant 211
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -57,7 +57,7 @@
num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 3], default=2)
use_batchnorm = self.use_batchnorm
if use_batchnorm is None:
- use_batchnorm = hp.Boolean('use_batchnorm', default=False)
+ use_batchnorm = hp.Boolean('use_batchnorm', default=True)
if self.dropout is not None:
dropout = self.dropout
else:
Mutant 212
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -57,7 +57,7 @@
num_layers = self.num_layers or hp.Choice('num_layers', [1, 2, 3], default=2)
use_batchnorm = self.use_batchnorm
if use_batchnorm is None:
- use_batchnorm = hp.Boolean('use_batchnorm', default=False)
+ use_batchnorm = None
if self.dropout is not None:
dropout = self.dropout
else:
Mutant 214
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -61,7 +61,7 @@
if self.dropout is not None:
dropout = self.dropout
else:
- dropout = hp.Choice('dropout', [0.0, 0.25, 0.5], default=0)
+ dropout = hp.Choice('XXdropoutXX', [0.0, 0.25, 0.5], default=0)
for i in range(num_layers):
units = hp.Choice(
Mutant 217
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -61,7 +61,7 @@
if self.dropout is not None:
dropout = self.dropout
else:
- dropout = hp.Choice('dropout', [0.0, 0.25, 0.5], default=0)
+ dropout = hp.Choice('dropout', [0.0, 0.25, 1.5], default=0)
for i in range(num_layers):
units = hp.Choice(
Mutant 220
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -65,7 +65,7 @@
for i in range(num_layers):
units = hp.Choice(
- 'units_{i}'.format(i=i),
+ 'XXunits_{i}XX'.format(i=i),
[16, 32, 64, 128, 256, 512, 1024],
default=32)
output_node = layers.Dense(units)(output_node)
Mutant 221
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -66,7 +66,7 @@
for i in range(num_layers):
units = hp.Choice(
'units_{i}'.format(i=i),
- [16, 32, 64, 128, 256, 512, 1024],
+ [17, 32, 64, 128, 256, 512, 1024],
default=32)
output_node = layers.Dense(units)(output_node)
if use_batchnorm:
Mutant 223
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -66,7 +66,7 @@
for i in range(num_layers):
units = hp.Choice(
'units_{i}'.format(i=i),
- [16, 32, 64, 128, 256, 512, 1024],
+ [16, 32, 65, 128, 256, 512, 1024],
default=32)
output_node = layers.Dense(units)(output_node)
if use_batchnorm:
Mutant 224
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -66,7 +66,7 @@
for i in range(num_layers):
units = hp.Choice(
'units_{i}'.format(i=i),
- [16, 32, 64, 128, 256, 512, 1024],
+ [16, 32, 64, 129, 256, 512, 1024],
default=32)
output_node = layers.Dense(units)(output_node)
if use_batchnorm:
Mutant 225
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -66,7 +66,7 @@
for i in range(num_layers):
units = hp.Choice(
'units_{i}'.format(i=i),
- [16, 32, 64, 128, 256, 512, 1024],
+ [16, 32, 64, 128, 257, 512, 1024],
default=32)
output_node = layers.Dense(units)(output_node)
if use_batchnorm:
Mutant 226
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -66,7 +66,7 @@
for i in range(num_layers):
units = hp.Choice(
'units_{i}'.format(i=i),
- [16, 32, 64, 128, 256, 512, 1024],
+ [16, 32, 64, 128, 256, 513, 1024],
default=32)
output_node = layers.Dense(units)(output_node)
if use_batchnorm:
Mutant 227
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -66,7 +66,7 @@
for i in range(num_layers):
units = hp.Choice(
'units_{i}'.format(i=i),
- [16, 32, 64, 128, 256, 512, 1024],
+ [16, 32, 64, 128, 256, 512, 1025],
default=32)
output_node = layers.Dense(units)(output_node)
if use_batchnorm:
Mutant 233
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -72,7 +72,7 @@
if use_batchnorm:
output_node = layers.BatchNormalization()(output_node)
output_node = layers.ReLU()(output_node)
- if dropout > 0:
+ if dropout >= 0:
output_node = layers.Dropout(dropout)(output_node)
return output_node
Mutant 234
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -72,7 +72,7 @@
if use_batchnorm:
output_node = layers.BatchNormalization()(output_node)
output_node = layers.ReLU()(output_node)
- if dropout > 0:
+ if dropout > 1:
output_node = layers.Dropout(dropout)(output_node)
return output_node
Mutant 236
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -253,7 +253,6 @@
output_node = layers.Dropout(dropout)(output_node)
return output_node
- @staticmethod
def _get_padding(kernel_size, output_node):
if all([kernel_size * 2 <= length
for length in output_node.shape[1:-1]]):
Mutant 237
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -274,7 +274,7 @@
def __init__(self,
head_size: Optional[int] = None,
- num_heads: Optional[int] = 8,
+ num_heads: Optional[int] = 9,
**kwargs):
super().__init__(**kwargs)
self.head_size = head_size
Mutant 238
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -343,7 +343,6 @@
) # (batch_size, seq_len, head_size)
return output
- @staticmethod
def attention(query, key, value):
score = tf.matmul(query, key, transpose_b=True)
dim_key = tf.cast(tf.shape(key)[-1], tf.float32)
Mutant 239
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -352,7 +352,6 @@
output = tf.matmul(weights, value)
return output, weights
- @staticmethod
def separate_heads(x, batch_size, num_heads, projection_dim):
x = tf.reshape(x, (batch_size, -1, num_heads, projection_dim))
return tf.transpose(x, perm=[0, 2, 1, 3])
Mutant 240
--- autokeras/blocks/basic.py
+++ autokeras/blocks/basic.py
@@ -489,7 +489,6 @@
output = layernorm2(add_inputs_2)
return output
- @staticmethod
def pos_array_funct(maxlen, batch_size):
pos_ones = tf.ones((batch_size, 1), dtype=tf.int32)
positions = tf.range(start=0, limit=maxlen, delta=1)