我正在尝试使用 Keras 自定义图层模板创建自定义损失函数。我无法执行类似于以下论文中提到的计算（第 4 页，方程 5）：https : //web.stanford.edu/class/archive/cs/cs224n/cs224n.1174/reports/2748045.pdf
class CustomLoss(Layer):
def __init__(self, **kwargs):
self.result = None
super(CustomLoss, self).__init__(**kwargs)
def build(self, input_shape):
# shape_ =
self.weight = self.add_weight(name='trainable_weight', shape=(input_shape[1], 4), initializer='glorot_uniform',
trainable=True)
self.bias = self.add_weight(name='trainable_bias', shape=(4, ), initializer='zeros', trainable=True)
super(CustomLoss, self).build(input_shape)
def call(self, input_vec, **kwargs):
v1 = input_vec[0] # This is of shape (None, m)
v2 = input_vec[1] # This is of shape (None, m)
v3 = K.square(input_vec[0] - input_vec[1])
v4 = K.dot(input_vec[0], input_vec[1])
vec = concatenate([v1, v2, v3, v4])
self.result = keras.layer.Softmax(keras.layers.ReLU(vec) * self.weight + self.bias)
return self.result
def compute_output_shape(self, input_shape):
return K.int_shape(self.result)
我收到以下错误：
TypeError: '>' not supported between instances of 'tuple' and 'float'
编辑1
num = 75
EMB_DIM = 300
SEN_LEN = 20
def base_network(_input):
embd = embedding_layer(V_SIZE, EMB_DIM, SEN_LEN, embedding_matrix(_input)
x = Bidirectional(lstm(num), merge_mode='concat')(embd)
x = Bidirectional(lstm(num), merge_mode='concat')(x)
x = Dropout(0.2)(x)
y = TimeDistributed(Dense(1, activation='tanh'))(x)
y = Flatten()(y)
y = Activation('softmax')(y)
y = RepeatVector(num * 2)(y)
y = Permute([2, 1]) (y)
z = multiply([x, y])
z = Lambda(lambda xin: K.sum(xin, axis=1))(z)
return z
inp1 = Input(shape=(SEN_LEN,), dtype='float32')
inp2 = Input(shape=(SEN_LEN,), dtype='float32')
s1 = base_network(inp1)
s2 = base_network(inp1)
sim_score = CustomLoss()([s1, s2])
output = concatenate([s1, s2 , sim_score])
d1 = Dense(2)(output)
sim = Dense(1, activation='sigmoid')(d1)
model = Model(inputs=[inp1, inp2], outputs=[sim])
model.compile(loss='mean_squared_error', optimizer=RMSprop())
编辑 2
TypeError Traceback (most recent call last)
<ipython-input-97-fa4c18ab9e4e> in <module>
7 s1 = base_network(input_2)
8
----> 9 sim_score = CustomLoss()([s1, s2])
10 output = concatenate([s1, s2 , sim_score])
11 d1 = Dense(2)(output)
~\.julia\conda\3\lib\site-packages\keras\engine\base_layer.py in __call__(self, inputs, **kwargs)
429 'You can build it manually via: '
430 '`layer.build(batch_input_shape)`')
--> 431 self.build(unpack_singleton(input_shapes))
432 self.built = True
433
<ipython-input-96-2f2fb52e16d0> in build(self, input_shape)
117 # shape_ =
118 self.weight = self.add_weight(name='trainable_weight', shape=(input_shape[1], 4, ), initializer='glorot_uniform',
--> 119 trainable=True)
120 self.bias = self.add_weight(name='trainable_bias', shape=(4, ), initializer='zeros', trainable=True)
121 super(CustomLoss, self).build(input_shape)
~\.julia\conda\3\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~\.julia\conda\3\lib\site-packages\keras\engine\base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint)
247 if dtype is None:
248 dtype = K.floatx()
--> 249 weight = K.variable(initializer(shape),
250 dtype=dtype,
251 name=name,
~\.julia\conda\3\lib\site-packages\keras\initializers.py in __call__(self, shape, dtype)
203 scale = self.scale
204 if self.mode == 'fan_in':
--> 205 scale /= max(1., fan_in)
206 elif self.mode == 'fan_out':
207 scale /= max(1., fan_out)
TypeError: '>' not supported between instances of 'tuple' and 'float'
编辑 3新错误：
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
~\.julia\conda\3\lib\site-packages\tensorflow\python\framework\ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1627 try:
-> 1628 c_op = c_api.TF_FinishOperation(op_desc)
1629 except errors.InvalidArgumentError as e:
InvalidArgumentError: Dimensions must be equal, but are 2000 and 500 for 'custom_loss_1/MatMul' (op: 'MatMul') with input shapes: [?,2000], [500,4].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-99-fa4c18ab9e4e> in <module>
7 s1 = base_network(input_2)
8
----> 9 sim_score = CustomLoss()([s1, s2])
10 output = concatenate([s1, s2 , sim_score])
11 d1 = Dense(2)(output)
~\.julia\conda\3\lib\site-packages\keras\engine\base_layer.py in __call__(self, inputs, **kwargs)
455 # Actually call the layer,
456 # collecting output(s), mask(s), and shape(s).
--> 457 output = self.call(inputs, **kwargs)
458 output_mask = self.compute_mask(inputs, previous_mask)
459
<ipython-input-98-23434db31b00> in call(self, x, **kwargs)
127 vec = concatenate([v1, v2, v3, v4])
128 # vec = K.Flatten(vec)
--> 129 self.result = keras.layer.Softmax(keras.layers.ReLU(vec) * self.weight + self.bias)
130 return self.result
131
~\.julia\conda\3\lib\site-packages\keras\backend\tensorflow_backend.py in dot(x, y)
1083 out = tf.sparse_tensor_dense_matmul(x, y)
1084 else:
-> 1085 out = tf.matmul(x, y)
1086 return out
1087
~\.julia\conda\3\lib\site-packages\tensorflow\python\ops\math_ops.py in matmul(a, b, transpose_a, transpose_b, adjoint_a, adjoint_b, a_is_sparse, b_is_sparse, name)
2055 else:
2056 return gen_math_ops.mat_mul(
-> 2057 a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
2058
2059
~\.julia\conda\3\lib\site-packages\tensorflow\python\ops\gen_math_ops.py in mat_mul(a, b, transpose_a, transpose_b, name)
4855 _, _, _op = _op_def_lib._apply_op_helper(
4856 "MatMul", a=a, b=b, transpose_a=transpose_a, transpose_b=transpose_b,
-> 4857 name=name)
4858 _result = _op.outputs[:]
4859 _inputs_flat = _op.inputs
~\.julia\conda\3\lib\site-packages\tensorflow\python\framework\op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
785 op = g.create_op(op_type_name, inputs, output_types, name=scope,
786 input_types=input_types, attrs=attr_protos,
--> 787 op_def=op_def)
788 return output_structure, op_def.is_stateful, op
789
~\.julia\conda\3\lib\site-packages\tensorflow\python\util\deprecation.py in new_func(*args, **kwargs)
486 'in a future version' if date is None else ('after %s' % date),
487 instructions)
--> 488 return func(*args, **kwargs)
489 return tf_decorator.make_decorator(func, new_func, 'deprecated',
490 _add_deprecated_arg_notice_to_docstring(
~\.julia\conda\3\lib\site-packages\tensorflow\python\framework\ops.py in create_op(***failed resolving arguments***)
3272 input_types=input_types,
3273 original_op=self._default_original_op,
-> 3274 op_def=op_def)
3275 self._create_op_helper(ret, compute_device=compute_device)
3276 return ret
~\.julia\conda\3\lib\site-packages\tensorflow\python\framework\ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1790 op_def, inputs, node_def.attr)
1791 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1792 control_input_ops)
1793
1794 # Initialize self._outputs.
~\.julia\conda\3\lib\site-packages\tensorflow\python\framework\ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1629 except errors.InvalidArgumentError as e:
1630 # Convert to ValueError for backwards compatibility.
-> 1631 raise ValueError(str(e))
1632
1633 return c_op
ValueError: Dimensions must be equal, but are 2000 and 500 for 'custom_loss_1/MatMul' (op: 'MatMul') with input shapes: [?,2000], [500,4].
这一行：
self.result = keras.layer.Softmax(keras.layers.ReLU(vec) * self.weight + self.bias)
你不能使用这样的层。而是使用等效的后端函数：
self.result = K.softmax(K.bias_add(K.dot(K.relu(vec), self.weight), self.bias, data_format='channels_last'))
注意：我没有读过这篇论文，所以还要确保你想relu在连接结果上应用 ，而不是在带有权重和添加偏差的点积之后。
更新：还有另一个问题是由您创建的权重形状引起的。由于您的层获得两个输入张量，因此方法的input_shape参数build将是两个元组的列表，即对应于每个输入张量的一个输入形状。因此，在创建权重时，shape=(input_shape[1], 4)您需要编写shape=(input_shape[0][1], 4).
更新 2：vec具有 的形状，(?, 2000)但weight具有 的形状，(500, 4)因此它们不能相乘。您可能想相应地调整形状weight：shape=(input_shape[0][1] * 4, 4)改为使用。
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