@tf_export('keras.metrics.mean_squared_error',
- 'keras.losses.mean_squared_error')
+ 'keras.metrics.mse',
+ 'keras.metrics.MSE',
+ 'keras.losses.mean_squared_error',
+ 'keras.losses.mse',
+ 'keras.losses.MSE')
def mean_squared_error(y_true, y_pred):
return K.mean(math_ops.square(y_pred - y_true), axis=-1)
@tf_export('keras.metrics.mean_absolute_error',
- 'keras.losses.mean_absolute_error')
+ 'keras.metrics.mae',
+ 'keras.metrics.MAE',
+ 'keras.losses.mean_absolute_error',
+ 'keras.losses.mae',
+ 'keras.losses.MAE')
def mean_absolute_error(y_true, y_pred):
return K.mean(math_ops.abs(y_pred - y_true), axis=-1)
@tf_export('keras.metrics.mean_absolute_percentage_error',
- 'keras.losses.mean_absolute_percentage_error')
+ 'keras.metrics.mape',
+ 'keras.metrics.MAPE',
+ 'keras.losses.mean_absolute_percentage_error',
+ 'keras.losses.mape',
+ 'keras.losses.MAPE')
def mean_absolute_percentage_error(y_true, y_pred):
diff = math_ops.abs(
(y_true - y_pred) / K.clip(math_ops.abs(y_true), K.epsilon(), None))
@tf_export('keras.metrics.mean_squared_logarithmic_error',
- 'keras.losses.mean_squared_logarithmic_error')
+ 'keras.metrics.msle',
+ 'keras.metrics.MSLE',
+ 'keras.losses.mean_squared_logarithmic_error',
+ 'keras.losses.msle',
+ 'keras.losses.MSLE')
def mean_squared_logarithmic_error(y_true, y_pred):
first_log = math_ops.log(K.clip(y_pred, K.epsilon(), None) + 1.)
second_log = math_ops.log(K.clip(y_true, K.epsilon(), None) + 1.)
@tf_export('keras.metrics.kullback_leibler_divergence',
- 'keras.losses.kullback_leibler_divergence')
+ 'keras.metrics.kld',
+ 'keras.metrics.KLD',
+ 'keras.losses.kullback_leibler_divergence',
+ 'keras.losses.kld',
+ 'keras.losses.KLD')
def kullback_leibler_divergence(y_true, y_pred):
y_true = K.clip(y_true, K.epsilon(), 1)
y_pred = K.clip(y_pred, K.epsilon(), 1)
return K.mean(y_pred - y_true * math_ops.log(y_pred + K.epsilon()), axis=-1)
-@tf_export('keras.metrics.cosine_proximity', 'keras.losses.cosine_proximity')
+@tf_export('keras.metrics.cosine_proximity',
+ 'keras.metrics.cosine',
+ 'keras.losses.cosine_proximity',
+ 'keras.losses.cosine')
def cosine_proximity(y_true, y_pred):
y_true = nn.l2_normalize(y_true, axis=-1)
y_pred = nn.l2_normalize(y_pred, axis=-1)
@tf_export("keras.initializers.Zeros", "initializers.zeros",
- "zeros_initializer")
+ "zeros_initializer", "keras.initializers.zeros")
class Zeros(Initializer):
"""Initializer that generates tensors initialized to 0."""
return {"dtype": self.dtype.name}
-@tf_export("keras.initializers.Ones", "initializers.ones", "ones_initializer")
+@tf_export("keras.initializers.Ones", "initializers.ones", "ones_initializer",
+ "keras.initializers.ones")
class Ones(Initializer):
"""Initializer that generates tensors initialized to 1."""
@tf_export("keras.initializers.Constant", "initializers.constant",
- "constant_initializer")
+ "constant_initializer", "keras.initializers.constant")
class Constant(Initializer):
"""Initializer that generates tensors with constant values.
@tf_export("keras.initializers.RandomUniform", "initializers.random_uniform",
- "random_uniform_initializer")
+ "random_uniform_initializer", "keras.initializers.uniform",
+ "keras.initializers.random_uniform")
class RandomUniform(Initializer):
"""Initializer that generates tensors with a uniform distribution.
@tf_export("keras.initializers.RandomNormal", "initializers.random_normal",
- "random_normal_initializer")
+ "random_normal_initializer", "keras.initializers.normal",
+ "keras.initializers.random_normal")
class RandomNormal(Initializer):
"""Initializer that generates tensors with a normal distribution.
@tf_export("keras.initializers.TruncatedNormal",
- "initializers.truncated_normal", "truncated_normal_initializer")
+ "initializers.truncated_normal", "truncated_normal_initializer",
+ "keras.initializers.truncated_normal")
class TruncatedNormal(Initializer):
"""Initializer that generates a truncated normal distribution.
@tf_export("keras.initializers.Orthogonal", "initializers.orthogonal",
- "orthogonal_initializer")
+ "orthogonal_initializer", "keras.initializers.orthogonal")
class Orthogonal(Initializer):
"""Initializer that generates an orthogonal matrix.
return self._dict_to_tensor(p, ksize, ksize, ksize)
-@tf_export("keras.initializers.Identity", "initializers.identity")
+@tf_export("keras.initializers.Identity", "initializers.identity",
+ "keras.initializers.identity")
class Identity(Initializer):
"""Initializer that generates the identity matrix.
--- /dev/null
+path: "tensorflow.keras.initializers.constant"
+tf_class {
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Constant\'>"
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Initializer\'>"
+ is_instance: "<type \'object\'>"
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'value\', \'dtype\', \'verify_shape\'], varargs=None, keywords=None, defaults=[\'0\', \"<dtype: \'float32\'>\", \'False\'], "
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+}
--- /dev/null
+path: "tensorflow.keras.initializers.identity"
+tf_class {
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Identity\'>"
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Initializer\'>"
+ is_instance: "<type \'object\'>"
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'gain\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \"<dtype: \'float32\'>\"], "
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+}
--- /dev/null
+path: "tensorflow.keras.initializers.normal"
+tf_class {
+ is_instance: "<class \'tensorflow.python.ops.init_ops.RandomNormal\'>"
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Initializer\'>"
+ is_instance: "<type \'object\'>"
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"<dtype: \'float32\'>\"], "
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+}
--- /dev/null
+path: "tensorflow.keras.initializers.ones"
+tf_class {
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Ones\'>"
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Initializer\'>"
+ is_instance: "<type \'object\'>"
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"<dtype: \'float32\'>\"], "
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+}
--- /dev/null
+path: "tensorflow.keras.initializers.orthogonal"
+tf_class {
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Orthogonal\'>"
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Initializer\'>"
+ is_instance: "<type \'object\'>"
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'gain\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \"<dtype: \'float32\'>\"], "
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+}
name: "Zeros"
mtype: "<type \'type\'>"
}
+ member {
+ name: "constant"
+ mtype: "<type \'type\'>"
+ }
+ member {
+ name: "identity"
+ mtype: "<type \'type\'>"
+ }
+ member {
+ name: "normal"
+ mtype: "<type \'type\'>"
+ }
+ member {
+ name: "ones"
+ mtype: "<type \'type\'>"
+ }
+ member {
+ name: "orthogonal"
+ mtype: "<type \'type\'>"
+ }
+ member {
+ name: "random_normal"
+ mtype: "<type \'type\'>"
+ }
+ member {
+ name: "random_uniform"
+ mtype: "<type \'type\'>"
+ }
+ member {
+ name: "truncated_normal"
+ mtype: "<type \'type\'>"
+ }
+ member {
+ name: "uniform"
+ mtype: "<type \'type\'>"
+ }
+ member {
+ name: "zeros"
+ mtype: "<type \'type\'>"
+ }
member_method {
name: "deserialize"
argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
--- /dev/null
+path: "tensorflow.keras.initializers.random_normal"
+tf_class {
+ is_instance: "<class \'tensorflow.python.ops.init_ops.RandomNormal\'>"
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Initializer\'>"
+ is_instance: "<type \'object\'>"
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"<dtype: \'float32\'>\"], "
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+}
--- /dev/null
+path: "tensorflow.keras.initializers.random_uniform"
+tf_class {
+ is_instance: "<class \'tensorflow.python.ops.init_ops.RandomUniform\'>"
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Initializer\'>"
+ is_instance: "<type \'object\'>"
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'minval\', \'maxval\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\', \"<dtype: \'float32\'>\"], "
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+}
--- /dev/null
+path: "tensorflow.keras.initializers.truncated_normal"
+tf_class {
+ is_instance: "<class \'tensorflow.python.ops.init_ops.TruncatedNormal\'>"
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Initializer\'>"
+ is_instance: "<type \'object\'>"
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"<dtype: \'float32\'>\"], "
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+}
--- /dev/null
+path: "tensorflow.keras.initializers.uniform"
+tf_class {
+ is_instance: "<class \'tensorflow.python.ops.init_ops.RandomUniform\'>"
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Initializer\'>"
+ is_instance: "<type \'object\'>"
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'minval\', \'maxval\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\', \"<dtype: \'float32\'>\"], "
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+}
--- /dev/null
+path: "tensorflow.keras.initializers.zeros"
+tf_class {
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Zeros\'>"
+ is_instance: "<class \'tensorflow.python.ops.init_ops.Initializer\'>"
+ is_instance: "<type \'object\'>"
+ member_method {
+ name: "__init__"
+ argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"<dtype: \'float32\'>\"], "
+ }
+ member_method {
+ name: "from_config"
+ argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "get_config"
+ argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+ }
+}
path: "tensorflow.keras.losses"
tf_module {
member_method {
+ name: "KLD"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "MAE"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "MAPE"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "MSE"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "MSLE"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
name: "binary_crossentropy"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
+ name: "cosine"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
name: "cosine_proximity"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
+ name: "kld"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
name: "kullback_leibler_divergence"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
+ name: "mae"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "mape"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
name: "mean_absolute_error"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
+ name: "mse"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "msle"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
name: "poisson"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
path: "tensorflow.keras.metrics"
tf_module {
member_method {
+ name: "KLD"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "MAE"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "MAPE"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "MSE"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "MSLE"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
name: "binary_accuracy"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
+ name: "cosine"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
name: "cosine_proximity"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
+ name: "kld"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
name: "kullback_leibler_divergence"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
+ name: "mae"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "mape"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
name: "mean_absolute_error"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}
member_method {
+ name: "mse"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
+ name: "msle"
+ argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
+ }
+ member_method {
name: "poisson"
argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
}