此处描述的神经网络也是为图像分类而创建的，用于使用TensorFlow API简单计算1个神经元的隐藏层的梯度检查 .
下面的代码基于TensorFlow Test Gradient Check example的参考
def check_gradients(feed_dict, num_features, neurons , output_size, cost):
batch = len(feed_dict[images_placeholder])
e = 1e-4
all_params = [
images_placeholder, #shape = (?,1024) ? == batch
tf.get_default_graph().get_tensor_by_name('Layer_Hidden/weights:0'), #shape=(1024, 1)
tf.get_default_graph().get_tensor_by_name('Layer_Hidden/biases:0'), #shape=(1,)
tf.get_default_graph().get_tensor_by_name('Layer_Output/weights:0'), #shape=(1, 10)
tf.get_default_graph().get_tensor_by_name('Layer_Output/biases:0'), #shape=(10,)
]
print (all_params[0])
param_sizes = [
[batch, num_features], # [100, 1024]
[num_features, neurons], # [1024, 1]
[neurons], # [1]
[neurons, output_size], # [1,10]
[output_size], # [10]
]
for param_index in range(len(all_params)):
diff = tf.test.compute_gradient_error(
all_params[param_index],
param_sizes[param_index],
cost,
[batch],
delta=e,
extra_feed_dict=feed_dict)
print('level:', param_index, ', Gradient Error:', diff)
显示了一些重要的代码段代码，以提高对提议结构的理解：
images_placeholder = tf.placeholder(tf.float32, shape=[None, 1024])
batch = 100
此计算的错误与 images_placeholder 形状有关，如上所述 .
InvalidArgumentError：重塑的输入是一个具有100个值的张量，但请求的形状有1 [[Node：gradients_1 / Loss / Add_grad / Reshape = Reshape [T = DT_FLOAT，Tshape = DT_INT32，_device =“/ job：localhost / replica ：0 /任务：0 / cpu：0“]（gradients_1 / Loss / Add_grad / Sum，gradients_1 / Loss / Add_grad / Shape）]]
我无法弄清楚出了什么问题，因为我在这里解决了这个问题 .
谢谢