我正在做一些关于PyTorch源代码的研究。在中,作者实际上删除了模块。
from .adadelta import Adadelta # noqa: F401
from .adagrad import Adagrad # noqa: F401
from .adam import Adam # noqa: F401
del adadelta
del adagrad
del adam
这样做的理由是什么?
当我运行我的代码时,我得到了以下输出: %Run run_img.py
/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.4 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5
return f(*args, **kwds)
/usr/lib/python3.5/importlib/_bootstrap.py:222:
我有一个问题,开始使用“紧张流学习”(以前称为skflow)。
我的问题?
我甚至不能运行最简单的DNN示例。
下面的示例引发一个错误
**RuntimeError: Init operations did not make model ready. Init op:
init, init fn: None, error: Variables not initialized: global_step,
linear/_weight..*
在jupyter笔记本中内核突然结束了?
我是漏掉了什么还是虫子?
from tensorflow.contrib import learn
from
我正在尝试使用Pandas的一些测试数据来训练一个简单的DNNClassifier。当TensorFlow尝试保存检查点时,它会遇到以下错误。
这是一个内部错误-手册中没有可用的信息。
INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.InternalError'>, Unable to get element as bytes.
INFO:tensorflow:Saving checkpoints for 0 into /tm
例如,Keras的Adagrad的实现是:
class Adagrad(Optimizer):
"""Adagrad optimizer.
It is recommended to leave the parameters of this optimizer
at their default values.
# Arguments
lr: float >= 0. Learning rate.
epsilon: float >= 0.
decay: float >= 0. Learning rate decay over each
我试图在tensorflow版本2.4.1上使用嵌入层并行化一个模型。但是它给我带来了以下错误:
InvalidArgumentError: Cannot assign a device for operation sequential/emb_layer/embedding_lookup/ReadVariableOp: Could not satisfy explicit device specification '' because the node {{colocation_node sequential/emb_layer/embedding_lookup/ReadVa
我在嵌入层上得到了一个InvalidArgumentError:
Colocation Debug Info:
Colocation group had the following types and supported devices:
Root Member(assigned_device_name_index_=2 requested_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' assigned_device_name_='/job:localhost/replica:0/task:0/
考虑一个简单的线拟合a * x + b = x,其中a,b是优化的参数,x是所给出的观测向量
import torch
X = torch.randn(1000,1,1)
可以立即看到,确切的解决方案是a=1,b=0,对于任何x,它可以很容易地找到,就像:
import numpy as np
np.polyfit(X.numpy().flatten(), X.numpy().flatten(), 1)
我现在试图通过梯度下降的方法在PyTorch中找到这个解,这里的均方误差被作为一个优化准则。
import matplotlib.pyplot as plt
import numpy as n
我在MovieLens数据集上使用一个模型。我想把两个序列组合在角点积中。但是,我得到了以下错误:
Layer dot_1 was called with an input that isn't a symbolic tensor. Received
type: <class 'keras.engine.sequential.Sequential'>. Full input:
[<keras.engine.sequential.Sequential object at 0x00000282DAFCC710>,
<keras.engin
我正在尝试构建一个只有一个层的自动编码器:
from keras import backend as K
def cost2(y_true, y_pred):
print "shapes:", model.get_weights()[0].shape
yy = K.dot( y_pred, model.get_weights()[0].T )
return np.sum((y_true - yy)**2)
x = Input(shape=(original_dim,))
y = Dense(latent_dim)(x)
model = Model(i