Microservices vs multi-layered architecture [closed]
来源： https://stackoverflow.com/questions/41837637/microservices-vs-multi-layered-architecture
multi-layer
How to use multilayered bidirectional LSTM in Tensorflow?
问题 I want to know how to use multilayered bidirectional LSTM in Tensorflow. I have already implemented the contents of bidirectional LSTM, but I wanna compare this model with the model added multi-layers. How should I add some code in this part? x = tf.unstack(tf.transpose(x, perm=[1, 0, 2])) #print(x[0].get_shape()) # Define lstm cells with tensorflow # Forward direction cell lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) # Backward direction cell lstm_bw_cell = rnn.BasicLSTMCell(n
MLP in tensorflow for regression… not converging
问题 Hello it is my first time working with tensorflow, i try to adapt the example here TensorFlow-Examples to use this code for regression problems with boston database. Basically, i only change the cost function ,the database, the inputs number, and the target number but when i run the MPL doesn't converge (i use a very low rate). I test it with Adam Optimization and descend gradient optimization but i have the same behavior. I appreciate your suggestions and ideas...!!! Observation: When i ran
MLP in tensorflow for regression… not converging
Hello it is my first time working with tensorflow, i try to adapt the example here TensorFlow-Examples to use this code for regression problems with boston database. Basically, i only change the cost function ,the database, the inputs number, and the target number but when i run the MPL doesn't converge (i use a very low rate). I test it with Adam Optimization and descend gradient optimization but i have the same behavior. I appreciate your suggestions and ideas...!!! Observation: When i ran this program without the modifications described above, the cost function value always decrease. Here
Tensorflow same code but get different result from CPU device to GPU device
问题 I am trying to implement a program to test the Tensorflow performance on GPU device. Data test is MNIST data, supervised training using Multilayer perceptron(Neural networks). I followed this simple example but I change the number of performance batch gradient to 10000 for i in range(10000) : batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step,feed_dict={x : batch_xs, y_ : batch_ys}) if i % 500 == 0: print(i) Eventually, when I check the predict accuracy using this code
Cannot stack LSTM with MultiRNNCell and dynamic_rnn
问题 I am trying to build a multivariate time series prediction model. I followed the following tutorial for temperature prediction. http://nbviewer.jupyter.org/github/addfor/tutorials/blob/master/machine_learning/ml16v04_forecasting_with_LSTM.ipynb I want to extend his model to multilayer LSTM model by using following code: cell = tf.contrib.rnn.LSTMCell(hidden, state_is_tuple=True) cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers,state_is_tuple=True) output, _ = tf.nn.dynamic_rnn(cell=cell
Cannot stack LSTM with MultiRNNCell and dynamic_rnn
I am trying to build a multivariate time series prediction model. I followed the following tutorial for temperature prediction. http://nbviewer.jupyter.org/github/addfor/tutorials/blob/master/machine_learning/ml16v04_forecasting_with_LSTM.ipynb I want to extend his model to multilayer LSTM model by using following code: cell = tf.contrib.rnn.LSTMCell(hidden, state_is_tuple=True) cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers,state_is_tuple=True) output, _ = tf.nn.dynamic_rnn(cell=cell, inputs=features, dtype=tf.float32) but I have an error saying: ValueError: Dimensions must be equal,
How to use multilayered bidirectional LSTM in Tensorflow?
I want to know how to use multilayered bidirectional LSTM in Tensorflow. I have already implemented the contents of bidirectional LSTM, but I wanna compare this model with the model added multi-layers. How should I add some code in this part? x = tf.unstack(tf.transpose(x, perm=[1, 0, 2])) #print(x[0].get_shape()) # Define lstm cells with tensorflow # Forward direction cell lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) # Backward direction cell lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) # Get lstm cell output try: outputs, _, _ = rnn.static_bidirectional_rnn(lstm