neural-network

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 am getting the following error while translating a column from spanish to English: JSONDecodeError: Expecting value: line 1 column 1 (char 0) My data frame looks like the following: case_id es fr 1234 - - 2345 Hola como estas? Encantada de conocerte comment vas-tu aujourd'hui 3456 Hola como estas? Encantada de conocerte - 123321 - comment vas-tu aujourd'hui '-' is something that shows that there are no comments. My data frame has a blank strings as well apart from comments so I have

问题 I am implementing Segnet in Python. Following is the code. img_w = 480 img_h = 360 pool_size = 2 def build_model(img_w, img_h, pool_size): n_labels = 12 kernel = 3 encoding_layers = [ Conv2D(64, (kernel, kernel), input_shape=(img_h, img_w, 3), padding='same'), BatchNormalization(), Activation('relu'), Convolution2D(64, (kernel, kernel), padding='same'), BatchNormalization(), Activation('relu'), MaxPooling2D(pool_size = (pool_size,pool_size)), Convolution2D(128, (kernel, kernel), padding='same

问题 I am new to R and I am trying to build a neural network for a regression task. I am using neuralnet library to construct my neuralnet and I notice it accepts several arguments. act.fct being one of it. act.fct a differentiable function that is used for smoothing the result of the cross product of the covariate or neurons and the weights. Additionally the strings, 'logistic' and 'tanh' are possible for the logistic function and tangent hyperbolicus. By default a logistic activation function is

问题 I am new to the deep learning toolbox in MATLAB and I am very confused about the difference between training function of network and the corresponding learning function of network parameters. For example, if I create a feedforward network with 7 hidden neurons and gradient descent training function: >> net = feedforwardnet(7,'traingd'); Then, we find what is the learning function of input weight for example: >> net.inputWeights{1}.learnFcn ans = 'learngdm' We find it gradient descent with

问题 I am following trask's article to build a bare bone neural network in Python. Though he builds 1 layer Network (that maps 3 inputs to a single output) and a 2 layer network (that has 3 inputs, 4 neuron hidden layer and output layer with single neuron). My task was to build a network that can approximate the function Y = X1 + X2 + X3 . I provide the network with Y and it guesses values of x1, x2 and x3. For this, I modified the above network. I tried to invert both above networks, i.e, tried

问题 I have the following network which works fine: output = LSTM(8)(output) output = Dense(2)(output) Now for the same model, I am trying to stack a few LSTM layer like below: output = LSTM(8)(output, return_sequences=True) output = LSTM(8)(output) output = Dense(2)(output) But I got the following errors: TypeError Traceback (most recent call last) <ipython-input-2-0d0ced2c7417> in <module>() 39 40 output = Concatenate(axis=2)([leftOutput,rightOutput]) ---> 41 output = LSTM(8)(output, return

问题 I have a trained Tensorflow 2.0 model (from tf.keras.Sequential()) that takes an input layer with 26 columns (X) and produces an output layer with 1 column (Y). In TF 1.x I was able to calculate the gradient of the output with respect to the input with the following: model = load_model('mymodel.h5') sess = K.get_session() grad_func = tf.gradients(model.output, model.input) gradients = sess.run(grad_func, feed_dict={model.input: X})[0] In TF2 when I try to run tf.gradients(), I get the error:

问题 I have a trained Tensorflow 2.0 model (from tf.keras.Sequential()) that takes an input layer with 26 columns (X) and produces an output layer with 1 column (Y). In TF 1.x I was able to calculate the gradient of the output with respect to the input with the following: model = load_model('mymodel.h5') sess = K.get_session() grad_func = tf.gradients(model.output, model.input) gradients = sess.run(grad_func, feed_dict={model.input: X})[0] In TF2 when I try to run tf.gradients(), I get the error: