recurrent-neural-network

I have read a sequence of images into a numpy array with shape (7338, 225, 1024, 3) where 7338 is the sample size, 225 are the time steps and 1024 (32x32) are flattened image pixels, in 3 channels (RGB). I have a sequential model with an LSTM layer: model = Sequential() model.add(LSTM(128, input_shape=(225, 1024, 3)) But this results in the error: Input 0 is incompatible with layer lstm_1: expected ndim=3, found ndim=4 The documentation mentions that the input tensor for LSTM layer should be a 3D tensor with shape (batch_size, timesteps, input_dim) , but in my case my input_dim is 2D. What is

My question and problem is stated below the two blocks of code. Loss Function def loss(labels, logits, sequence_lengths, label_lengths, logit_lengths): scores = [] for i in xrange(runner.batch_size): sequence_length = sequence_lengths[i] for j in xrange(length): label_length = label_lengths[i, j] logit_length = logit_lengths[i, j] # get top k indices <==> argmax_k(labels[i, j, 0, :], label_length) top_labels = np.argpartition(labels[i, j, 0, :], -label_length)[-label_length:] top_logits = np.argpartition(logits[i, j, 0, :], -logit_length)[-logit_length:] scores.append(edit_distance(top_labels,

I'm trying to fit an RNN in Keras using sequences that have varying time lengths. My data is in a Numpy array with format (sample, time, feature) = (20631, max_time, 24) where max_time is determined at run-time as the number of time steps available for the sample with the most time stamps. I've padded the beginning of each time series with 0 , except for the longest one, obviously. I've initially defined my model like so... model = Sequential() model.add(Masking(mask_value=0., input_shape=(max_time, 24))) model.add(LSTM(100, input_dim=24)) model.add(Dense(2)) model.add(Activation(activate))

What is the upside of using tf.nn.rnn instead of tf.nn.dynamic_rnn ? The documentation says : [ dynamic_rnn ] is functionally identical to the function rnn above, but performs fully dynamic unrolling of inputs. Is there any case where one might prefer to use tf.nn.rnn instead of tf.nn.dynamic_rnn ? 来源： https://stackoverflow.com/questions/42356027/what-is-the-upside-of-using-tf-nn-rnn-instead-of-tf-nn-dynamic-rnn-in-tensor

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

I have an LSTM in Keras that I am training to predict on time series data. I want the network to output predictions on each timestep, as it will receive a new input every 15 seconds. So what I am struggling with is the proper way to train it so that it will output h_0, h_1, ..., h_t, as a constant stream as it receives x_0, x_1, ...., x_t as a stream of inputs. Is there a best practice for doing this? You can enable statefulness in your LSTM layers by setting stateful=True . This changes the behavior of the layer to always use the state of the previous invocation of the layer instead of