recurrent-neural-network

I'm trying to run this SimpleRNN: model.add(SimpleRNN(init='uniform',output_dim=1,input_dim=len(pred_frame.columns))) model.compile(loss="mse", optimizer="sgd") model.fit(X=predictor_train, y=target_train, batch_size=len(pred_frame.index),show_accuracy=True) The error is on model.fit, as you can see below: File "/Users/file.py", line 1496, in Pred model.fit(X=predictor_train, y=target_train, batch_size=len(pred_frame.index),show_accuracy=True) File "/Library/Python/2.7/site-packages/keras/models.py", line 581, in fit shuffle=shuffle, metrics=metrics) File "/Library/Python/2.7/site-packages

I'm training an RNN using keras and would like to see how the validation accuracy changes with the data set size. Keras has a list called val_acc in its history object which gets appended after every epoch with the respective validation set accuracy ( link to the post in google group ). I want to get the average of val_acc for the number of epochs run and plot that against the respective data set size. Question: How can I retrieve the elements in the val_acc list and perform an operation like numpy.mean(val_acc) ? EDIT: As @runDOSrun said, getting the mean of the val_acc s doesn't make sense.

I'm trying to pass the output of one layer into two different layers and then join them back together. However, I'm being stopped by this error which is telling me that my input isn't a symbolic tensor. Received type: <class 'keras.layers.recurrent.LSTM'>. All inputs to the layers should be tensors. However, I believe I'm following the documentation quite closely: https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models and am not entirely sure why this is wrong? net_input = Input(shape=(maxlen, len(chars)), name='net_input') lstm_out = LSTM(128, input_shape=

Currently, I am using the default 64 as the batch size for the seq2seq tensorflow model. What is the maximum batch size , layer size etc I can go with a single Titan X GPU with 12 GB RAM with Haswell-E xeon 128GB RAM. The input data is converted to embeddings. Following are some helpful parameters I am using , it seems the cell input size is 1024: encoder_inputs: a list of 2D Tensors [batch_size x cell.input_size]. decoder_inputs: a list of 2D Tensors [batch_size x cell.input_size]. tf.app.flags.DEFINE_integer("size", 1024, "Size of each model layer.") So based on my hardware what is the

I would like to build a toy LSTM model for regression. This nice tutorial is already too complicated for a beginner. Given a sequence of length time_steps , predict the next value. Consider time_steps=3 and the sequences: array([ [[ 1.], [ 2.], [ 3.]], [[ 2.], [ 3.], [ 4.]], ... the target values should be: array([ 4., 5., ... I define the following model: # Network Parameters time_steps = 3 num_neurons= 64 #(arbitrary) n_features = 1 # tf Graph input x = tf.placeholder("float", [None, time_steps, n_features]) y = tf.placeholder("float", [None, 1]) # Define weights weights = { 'out': tf

Do RNNs learn different dependency patterns when the input is batch-major as opposed to time-major? (Edit: sorry my initial argument was why it makes sense but I realized that it doesn't so this is a little OT.) I haven't found the TF-groups reasoning behind this but it does does not make computational sense as ops are written in C++. Intuitively, we want to mash up (multiply/add etc) different features from the same sequence on the same timestep. Different timesteps can’t be done in parallell while batch/sequences can so feature>batch/sequence>timestep. By default Numpy and C++ uses row-major