![](/img/trans.png)
[英]Keras Lambda layer to perform a maximum and output in (?, 1) shape
[英]Lambda layer to perform if then in keras/tensorflow
我正在用這個把頭發扯掉。
我在這里問了一個問題, 如果然后在自定義的不可訓練的 keras 層中,但我仍然遇到困難。
我嘗試了他的解決方案,但沒有用 - 我想我會用他的解決方案發布我的完整代碼
我有一個自定義 Keras 層,我想從特定輸入返回特定輸出。 我不希望它是可訓練的。
該層應執行以下操作
if input = [1,0] then output = 1
if input = [0,1] then output = 0
這是用於執行此操作的 lambda 層代碼:
input_tensor = Input(shape=(n_hots,))
def custom_layer_1(tensor):
if tensor == [1,0]:
resp_1 = np.array([1,],dtype=np.int32)
k_resp_1 = backend.variable(value=resp_1)
return k_resp_1
elif tensor == [0,1]:
resp_0 = np.array([0,],dtype=np.int32)
k_resp_0 = backend.variable(value=resp_0)
return k_resp_0
else:
resp_e = np.array([-1,])
k_resp_e = backend.variable(value=resp_e)
return k_resp_e
print(tensor.shape)
layer_one = keras.layers.Lambda(custom_layer_1,output_shape = (None,))(input_tensor)
_model = Model(inputs=input_tensor, outputs = layer_one)
當我擬合我的模型時,盡管有輸入,它總是計算 -1。
這是模型的樣子:
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 2) 0
_________________________________________________________________
lambda_1 (Lambda) (None, None) 0
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
這是模型的完整代碼:
import numpy as np
from keras.models import Model
from keras import layers
from keras import Input
from keras import backend
import keras
from keras import models
import tensorflow as tf
# Generate the datasets:
n_obs = 1000
n_hots = 2
obs_mat = np.zeros((n_obs,n_hots),dtype=np.int32)
resp_mat = np.zeros((n_obs,1),dtype=np.int32)
# which position in the array should be "hot" ?
hot_locs = np.random.randint(n_hots, size=n_obs)
# set the bits:
for row,loc in zip(np.arange(n_obs),hot_locs):
obs_mat[row,loc] = 1
for idx in np.arange(n_obs):
if( (obs_mat[idx,:]==[1,0]).all() == True ):
resp_mat[idx] = 1
if( (obs_mat[idx,:]==[0,1]).all() == True ):
resp_mat[idx] = 0
# test data:
test_suite = np.identity(n_hots)
# Build the network
input_tensor = Input(shape=(n_hots,))
def custom_layer_1(tensor):
if tensor == [1,0]:
resp_1 = np.array([1,],dtype=np.int32)
k_resp_1 = backend.variable(value=resp_1)
return k_resp_1
elif tensor == [0,1]:
resp_0 = np.array([0,],dtype=np.int32)
k_resp_0 = backend.variable(value=resp_0)
return k_resp_0
else:
resp_e = np.array([-1,])
k_resp_e = backend.variable(value=resp_e)
return k_resp_e
print(tensor.shape)
layer_one = keras.layers.Lambda(custom_layer_1,output_shape = (None,))(input_tensor)
_model = Model(inputs=input_tensor, outputs = layer_one)
# compile
_model.compile(optimizer="adam",loss='mse')
#train (even thought there's nothing to train)
history_mdl = _model.fit(obs_mat,resp_mat,verbose=True,batch_size = 100,epochs = 10)
# test
_model.predict(test_suite)
# outputs: array([-1., -1.], dtype=float32)
test = np.array([1,0])
test = test.reshape(1,2)
_model.predict(test,verbose=True)
# outputs: -1
這看起來很簡單的東西,為什么它不起作用? 謝謝
有幾個原因:
(samples, hots)
與 1D 張量(hots)
。if
tf
是一個張量框架,你可能不會用普通的方式獲得好的結果。所以,建議是:
from keras import backend as K
def custom_layer(tensor):
#comparison tensors with compatible shape 2D: (dummy_batch, hots)
t10 = K.reshape(K.constant([1,0]), (1,2))
t01 = K.reshape(K.constant([0,1]), (1,2))
#comparison results - elementwise - shape (batch_size, 2)
is_t10 = K.equal(tensor, t10)
is_t01 = K.equal(tensor, t01)
#comparison results - per sample - shape (batch_size,)
is_t10 = K.all(is_t10, axis=-1)
is_t01 = K.all(is_t01, axis=-1)
#result options
zeros = K.zeros_like(is_t10, dtype='float32') #shape (batch_size,)
ones = K.ones_like(is_t10, dtype='float32') #shape (batch_size,)
negatives = -ones #shape (batch_size,)
#selecting options
result_01_or_else = K.switch(is_t01, zeros, negatives)
result = K.switch(is_t10, ones, result_01_or_else)
return result
警告:
- 此層不可微(它返回常量)-您將無法訓練此層之前的任何內容,如果您嘗試,您將收到“一個操作
None
梯度”錯誤。- 輸入
tensor
不能是其他層的輸出,因為您需要它是精確的 1 或 0。
聲明:本站的技術帖子網頁,遵循CC BY-SA 4.0協議,如果您需要轉載,請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.