我正在尝试将具有自定义层的Keras模型（.h5 / json）转换为张量流模型（.pb），并使用以下自定义LSTM层。
class AttentionVLSTMCell(AbstractRNNCell):
def __init__(...):
super(AttentionVLSTMCell, self).__init__(**kwargs)
...
@property
def state_size(self):
return [self.timestep*self.att_input_dim, self.units]
def build(self, input_shape): # difinition of the weights
...
@tf.function
def _time_distributed_dense(self, x, w, b=None, dropout=None,
input_dim=None, output_dim=None,
timesteps=None, training=None):
if input_dim is None:
input_dim = K.shape(x)[2]
if timesteps is None:
timesteps = K.shape(x)[1]
if output_dim is None:
output_dim = K.shape(w)[1]
if dropout is not None and 0. < dropout < 1.:
# apply the same dropout pattern at every timestep
ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
dropout_matrix = K.dropout(ones, dropout)
expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
x = K.in_train_phase(x * expanded_dropout_matrix, x, training=training)
# collapse time dimension and batch dimension together
x = K.reshape(x, (-1, input_dim))
x = K.dot(x, w)
if b is not None:
x = K.bias_add(x, b)
# reshape to 3D tensor
if K.backend() == 'tensorflow':
x = K.reshape(x, K.stack([-1, timesteps, output_dim]))
x.set_shape([None, None, output_dim])
else:
x = K.reshape(x, (-1, timesteps, output_dim))
return x
def _compute_update_vertex(self, x, V_tm1, c):
"""Computes carry and output using split kernels."""
# x = W * track
x_i, x_f, x_c, x_o = x
V_tm1_i, V_tm1_f, V_tm1_c, V_tm1_o, V_tm1_o2, V_tm1_u, V_tm1_v = V_tm1
c_i, c_f, c_c, c_o = c
# i = x_i + V_tm1_i * R_i
# = W_i * track + V_tm1_i * R_i + C_i * context
print(x_i)
print(V_tm1_i)
print(c_i)
i = self.recurrent_activation(
x_i
+ K.dot(V_tm1_i, self.recurrent_kernel[:, :self.units])
+ K.dot(c_i, self.context_kernel[:, :self.units]))
f = self.recurrent_activation(
x_f
+ K.dot(V_tm1_f, self.recurrent_kernel[:, self.units:self.units * 2])
+ K.dot(c_f, self.context_kernel[:, self.units:self.units * 2]))
c = self.activation(
x_c
+ K.dot(V_tm1_c, self.recurrent_kernel[:, self.units * 2:self.units * 3])
+ K.dot(c_c, self.context_kernel[:, self.units * 2:self.units * 3]))
# U = update vertex
U = f * V_tm1_u + i * c
o = self.recurrent_activation(
x_o
+ K.dot(V_tm1_o, self.recurrent_kernel[:, self.units * 3:])
+ K.dot(c_o, self.context_kernel[:, self.units * 3:]))
h_temp = o * self.activation(V_tm1_o2)
# h size [self.units]
h = self.dense_activation(K.dot(h_temp, self.dense_kernel))
# h size [1] activated sigmoid
#the size of h is [units]
V = h * U + (1-h) * V_tm1_v
return h, V
def call(self, inputs, states, training=None):
# store the whole sequence so we can "attend" to it at each timestep
att = states[0] # Attention input (track num, input dim)
self.x_seq = K.reshape(att, (-1, self.timestep, self.att_input_dim)) # Attention input (track num, input dim)
V_tm1 = states[1] # previous Vertex state (units)
# Additive Attention Bahdanau et al., 2015
# apply the a dense layer over the time dimension of the sequence
# do it here because it doesn't depend on any previous steps
# thefore we can save computation time:
self._uxpb = self._time_distributed_dense(self.x_seq,
self.attention_kernel_U,
b=self.attention_kernel_b,
input_dim=self.att_input_dim,
timesteps=self.timestep,
output_dim=self.units)
# repeat the input track to the length of the sequence (track num, feature dim))
_tt = K.repeat(inputs, self.timestep)
_Wxtt = K.dot(_tt, self.attention_kernel_W)
et = K.dot(activations.tanh(_Wxtt + self._uxpb), K.expand_dims(self.attention_kernel_V))
"""
#Dot-Product Attention Luong et al., 2015 / Scaled Dot-Product Attention Vaswani 2017
self.x_seq /= np.sqrt(self.att_input_dim)
et = K.batch_dot(K.expand_dims(inputs), self.x_seq, axes=[1, 2])
et = K.reshape(et, (-1, self.timestep, 1))
"""
at = K.exp(et)
at_sum = K.sum(at, axis=1)
at_sum_repeated = K.repeat(at_sum, self.timestep)
at /= at_sum_repeated # attention weights ({batchsize}, track num, 1)
context = K.squeeze(K.batch_dot(at, self.x_seq, axes=1), axis=1)
if self.implementation == 1:
# input = track
inputs_i = inputs
inputs_f = inputs
inputs_c = inputs
inputs_o = inputs
# k = W
k_i, k_f, k_c, k_o = array_ops.split(
self.kernel, num_or_size_splits=4, axis=1)
x_i = K.dot(inputs_i, k_i)
x_f = K.dot(inputs_f, k_f)
x_c = K.dot(inputs_c, k_c)
x_o = K.dot(inputs_o, k_o)
if self.use_bias:
b_i, b_f, b_c, b_o = array_ops.split(
self.bias, num_or_size_splits=4, axis=0)
x_i = K.bias_add(x_i, b_i)
x_f = K.bias_add(x_f, b_f)
x_c = K.bias_add(x_c, b_c)
x_o = K.bias_add(x_o, b_o)
V_tm1_i = V_tm1
V_tm1_f = V_tm1
V_tm1_c = V_tm1
V_tm1_o = V_tm1
V_tm1_o2 = V_tm1
V_tm1_u = V_tm1
V_tm1_v = V_tm1
c_i = context
c_f = context
c_c = context
c_o = context
x = (x_i, x_f, x_c, x_o)
V_tm1 = (V_tm1_i, V_tm1_f, V_tm1_c, V_tm1_o, V_tm1_o2, V_tm1_u, V_tm1_v)
c = (c_i, c_f, c_c, c_o)
h, V = self._compute_update_vertex(x, V_tm1, c)
return [h, at], [att, V]
def get_config(self):
...
def get_initial_state(self, inputs=True, batch_size=True, dtype=None):
...
我可以使用自定义图层加载模型，也可以在tensorflow.keras上使用它。
但是，当我尝试loaded_model.save(model_name, save_format="tf")
Traceback (most recent call last):
File "/home/usr7/s70087f/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 1619, in _create_c_op
c_op = c_api.TF_FinishOperation(op_desc)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape must be rank 2 but is rank 1 for 'MatMul_7' (op: 'MatMul') with input shapes: [256], [256,256].
V_tm1_i（注意LSTM输入门的计算）存在问题：
i = self.recurrent_activation(
x_i
+ K.dot(V_tm1_i, self.recurrent_kernel[:, :self.units])
+ K.dot(c_i, self.context_kernel[:, :self.units]))
我知道我们必须对'MatMul'使用张量> = 2 ndims，但实际上它具有批量大小尺寸。
因此，我希望它成为[None,256], [256,256]。
显示自定义层的输入和隐藏状态：
AttentionVLSTM
inputs
Tensor("model/Decoder_Attention_VLSTM/strided_slice_1:0", shape=(None, 256), dtype=float32)
states
(<tf.Tensor 'model/reshape/Reshape:0' shape=(None, 27136) dtype=float32>, <tf.Tensor 'model/Decoder_Activation_2/Relu:0' shape=(None, 256) dtype=float32>)
AttentionVLSTM
inputs
Tensor("TensorArrayV2Read/TensorListGetItem:0", shape=(None, 256), dtype=float32)
states
(<tf.Tensor 'Placeholder_3:0' shape=(None, 27136) dtype=float32>, <tf.Tensor 'Placeholder_4:0' shape=(None, 256) dtype=float32>)
AttentionVLSTM
inputs
Tensor("inputs:0", shape=(None, 256), dtype=float32)
states
(<tf.Tensor 'states:0' shape=(27136,) dtype=float32>, <tf.Tensor 'states_1:0' shape=(256,) dtype=float32>)
V_tm1是从隐藏状态获得的，并且“ Placeholder_4：0”是正确的形状[None,256]，但是“ states_1：0”失去了批处理轴[256,]的尺寸。
其他自定义层（此层除外）可以运行，并且该层也可以在tensorflow.Keras中工作。
批量大小= 32（状态0和输入：0不输出）
AttentionVLSTM
inputs
Tensor("model/Decoder_Attention_VLSTM/strided_slice_1:0", shape=(32, 256), dtype=float32)
states
(<tf.Tensor 'model/reshape/Reshape:0' shape=(32, 27136) dtype=float32>, <tf.Tensor 'model/Decoder_Activation_2/Relu:0' shape=(32, 256) dtype=float32>)
AttentionVLSTM
inputs
Tensor("TensorArrayV2Read/TensorListGetItem:0", shape=(32, 256), dtype=float32)
states
(<tf.Tensor 'Placeholder_3:0' shape=(32, 27136) dtype=float32>, <tf.Tensor 'Placeholder_4:0' shape=(32, 256) dtype=float32>)
答案 0 :(得分：0)
我可以解决这个问题。错误的原因是在此自定义类的末尾定义了初始状态。不必要，因为初始状态已在“ AbstractRNNCell”处定义。