mean-square-error

问题 I am currently implementing a custom loss layer and in the process, I stumbled upon the implementation of mean squared error in the objectives.py file [1]. I know I'm missing something in my understanding of this loss calculation because I always thought that the average was done separately across the samples for each output in each mini-batch (axis 0 of the tensor) but it appears that the average is actually being done across the last axis, which in a single vector, would mean it's being

问题 I'm training a neural network model for forecasting. Loss function is Mean Squared Error (MSE). However, I found that MSE calculated by Keras is much different from one calculated by Scikit-learn. Epoch 1/10 162315/162315 [==============================] - 14s 87us/step - loss: 111.8723 - mean_squared_error: 111.8723 - val_loss: 9.5308 - val_mean_squared_error: 9.5308 Epoch 00001: loss improved from inf to 111.87234, saving model to /home/Model/2019.04.26.10.55 Scikit Learn MSE = 208.811126

问题 I'm training a neural network model for forecasting. Loss function is Mean Squared Error (MSE). However, I found that MSE calculated by Keras is much different from one calculated by Scikit-learn. Epoch 1/10 162315/162315 [==============================] - 14s 87us/step - loss: 111.8723 - mean_squared_error: 111.8723 - val_loss: 9.5308 - val_mean_squared_error: 9.5308 Epoch 00001: loss improved from inf to 111.87234, saving model to /home/Model/2019.04.26.10.55 Scikit Learn MSE = 208.811126

问题 I'm training a neural network model for forecasting. Loss function is Mean Squared Error (MSE). However, I found that MSE calculated by Keras is much different from one calculated by Scikit-learn. Epoch 1/10 162315/162315 [==============================] - 14s 87us/step - loss: 111.8723 - mean_squared_error: 111.8723 - val_loss: 9.5308 - val_mean_squared_error: 9.5308 Epoch 00001: loss improved from inf to 111.87234, saving model to /home/Model/2019.04.26.10.55 Scikit Learn MSE = 208.811126

问题 How is Accuracy defined when the loss function is mean square error? Is it mean absolute percentage error? The model I use has output activation linear and is compiled with loss= mean_squared_error model.add(Dense(1)) model.add(Activation('linear')) # number model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy']) and the output looks like this: Epoch 99/100 1000/1000 [==============================] - 687s 687ms/step - loss: 0.0463 - acc: 0.9689 - val_loss: 3.7303 -

问题 How is Accuracy defined when the loss function is mean square error? Is it mean absolute percentage error? The model I use has output activation linear and is compiled with loss= mean_squared_error model.add(Dense(1)) model.add(Activation('linear')) # number model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy']) and the output looks like this: Epoch 99/100 1000/1000 [==============================] - 687s 687ms/step - loss: 0.0463 - acc: 0.9689 - val_loss: 3.7303 -

问题 How is Accuracy defined when the loss function is mean square error? Is it mean absolute percentage error? The model I use has output activation linear and is compiled with loss= mean_squared_error model.add(Dense(1)) model.add(Activation('linear')) # number model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy']) and the output looks like this: Epoch 99/100 1000/1000 [==============================] - 687s 687ms/step - loss: 0.0463 - acc: 0.9689 - val_loss: 3.7303 -

问题 I am getting this error "operands could not be broadcast together with shapes" for this code import numpy as np from sklearn.datasets import load_boston from sklearn.linear_model import LinearRegression beantown = load_boston() x=beantown.data y=beantown.target model = LinearRegression() model = model.fit(x,y) def mse(truth, predictions): return ((truth - predictions) ** 2).mean(None) print model.score(x,y) print mse(x,y) The error is on the line print mse(x,y) Error is ValueError: operands

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 2 years ago . Hello I have a network that produces logits / outputs like this: logits = tf.placeholder(tf.float32, [None, 128, 64, 64]) // outputs y = tf.placeholder(tf.float32, [None, 128, 64, 64]) // ground_truth, targets --> y ground truth values are downscaled from [0, 255] to [0, 1] in order to increase

问题 For a very simple classification problem where I have a target vector [0,0,0,....0] and a prediction vector [0,0.1,0.2,....1] would cross-entropy loss converge better/faster or would MSE loss? When I plot them it seems to me that MSE loss has a lower error margin. Why would that be? Or for example when I have the target as [1,1,1,1....1] I get the following: 回答1: You sound a little confused... Comparing the values of MSE & cross-entropy loss and saying that one is lower than the other is like