When using a Keras LSTM to predict on time series data I've been getting errors when I'm trying to train the model using a batch size of 50, while then trying to predict on the same model using a batch size of 1 (ie just predicting the next value).
Why am I not able to train and fit the model with multiple batches at once, and then use that model to predict for anything other than the same batch size. It doesn't seem to make sense, but then I could easily be missing something about this.
Edit: this is the model. batch_size is 50, sl is sequence length, which is set at 20 currently.
model = Sequential()
model.add(LSTM(1, batch_input_shape=(batch_size, 1, sl), stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=epochs, batch_size=batch_size, verbose=2)
here is the line for predicting on the training set for RMSE
# make predictions
trainPredict = model.predict(trainX, batch_size=batch_size)
here is the actual prediction of unseen time steps
for i in range(test_len):
print('Prediction %s: ' % str(pred_count))
next_pred_res = np.reshape(next_pred, (next_pred.shape[1], 1, next_pred.shape[0]))
# make predictions
forecastPredict = model.predict(next_pred_res, batch_size=1)
forecastPredictInv = scaler.inverse_transform(forecastPredict)
forecasts.append(forecastPredictInv)
next_pred = next_pred[1:]
next_pred = np.concatenate([next_pred, forecastPredict])
pred_count += 1
This issue is with the line:
forecastPredict = model.predict(next_pred_res, batch_size=batch_size)
The error when batch_size here is set to 1 is:
ValueError: Cannot feed value of shape (1, 1, 2) for Tensor 'lstm_1_input:0', which has shape '(10, 1, 2)' which is the same error that throws when batch_size here is set to 50 like the other batch sizes as well.
The total error is:
forecastPredict = model.predict(next_pred_res, batch_size=1)
File "/home/entelechy/tf_keras/lib/python3.5/site-packages/keras/models.py", line 899, in predict
return self.model.predict(x, batch_size=batch_size, verbose=verbose)
File "/home/entelechy/tf_keras/lib/python3.5/site-packages/keras/engine/training.py", line 1573, in predict
batch_size=batch_size, verbose=verbose)
File "/home/entelechy/tf_keras/lib/python3.5/site-packages/keras/engine/training.py", line 1203, in _predict_loop
batch_outs = f(ins_batch)
File "/home/entelechy/tf_keras/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 2103, in __call__
feed_dict=feed_dict)
File "/home/entelechy/tf_keras/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 767, in run
run_metadata_ptr)
File "/home/entelechy/tf_keras/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 944, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (1, 1, 2) for Tensor 'lstm_1_input:0', which has shape '(10, 1, 2)'
Edit: Once I set the model to stateful=False then I am able to use different batch sizes for fitting/training and prediction. What is the reason for this?
Unfortunately what you want to do is impossible with Keras ... I've also struggle a lot of time on this problems and the only way is to dive into the rabbit hole and work with Tensorflow directly to do LSTM rolling prediction.
First, to be clear on terminology, batch_size usually means number of sequences that are trained together, and num_steps means how many time steps are trained together. When you mean batch_size=1 and "just predicting the next value", I think you meant to predict with num_steps=1.
Otherwise, it should be possible to train and predict with batch_size=50 meaning you are training on 50 sequences and make 50 predictions every time step, one for each sequence (meaning training/prediction num_steps=1).
However, I think what you mean is that you want to use stateful LSTM to train with num_steps=50 and do prediction with num_steps=1. Theoretically this make senses and should be possible, and it is possible with Tensorflow, just not Keras.
The problem: Keras requires an explicit batch size for stateful RNN. You must specify batch_input_shape (batch_size, num_steps, features).
The reason: Keras must allocate a fixed-size hidden state vector in the computation graph with shape (batch_size, num_units) in order to persist the values between training batches. On the other hand, when stateful=False, the hidden state vector can be initialized dynamically with zeroes at the beginning of each batch so it does not need to be a fixed size. More details here: http://philipperemy.github.io/keras-stateful-lstm/
Possible work around: Train and predict with num_steps=1. Example: https://github.com/keras-team/keras/blob/master/examples/lstm_stateful.py. This might or might not work at all for your problem as the gradient for back propagation will be computed on only one time step. See: https://github.com/fchollet/keras/issues/3669
My solution: use Tensorflow: In Tensorflow you can train with batch_size=50, num_steps=100, then do predictions with batch_size=1, num_steps=1. This is possible by creating a different model graph for training and prediction sharing the same RNN weight matrices. See this example for next-character prediction: https://github.com/sherjilozair/char-rnn-tensorflow/blob/master/model.py#L11 and blog post http://karpathy.github.io/2015/05/21/rnn-effectiveness/. Note that one graph can still only work with one specified batch_size, but you can setup multiple model graphs sharing weights in Tensorflow.
Sadly what you wish for is impossible because you specify the batch_size when you define the model...
However, I found a simple way around this problem: create 2 models! The first is used for training and the second for predictions, and have them share weights:
train_model = Sequential([Input(batch_input_shape=(batch_size,...),
<continue specifying your model>])
predict_model = Sequential([Input(batch_input_shape=(1,...),
<continue specifying exact same model>])
train_model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam())
predict_model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam())
Now you can use any batch size you want. after you fit your train_model just save it's weights and load them with the predict_model:
train_model.save_weights('lstm_model.h5')
predict_model.load_weights('lstm_model.h5')
notice that you only want to save and load the weights, and not the whole model (which includes the architecture, optimizer etc...). This way you get the weights but you can input one batch at a time...
more on keras save/load models:
https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model
notice that you need to install h5py to use "save weights".
Another easy workaround is:
def create_model(batch_size):
model = Sequential()
model.add(LSTM(1, batch_input_shape=(batch_size, 1, sl), stateful=True))
model.add(Dense(1))
return model
model_train = create_model(batch_size=50)
model_train.compile(loss='mean_squared_error', optimizer='adam')
model_train.fit(trainX, trainY, epochs=epochs, batch_size=batch_size)
model_predict = create_model(batch_size=1)
weights = model_train.get_weights()
model_predict.set_weights(weights)
The best solution to this problem is "Copy Weights". It can be really helpful if you want to train & predict with your LSTM model with different batch sizes.
For example, once you have trained your model with 'n' batch size as shown below:
# configure network
n_batch = len(X)
n_epoch = 1000
n_neurons = 10
# design network
model = Sequential()
model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
And now you want to want predict values fewer than your batch size where n=1.
What you can do is that, copy the weights of your fit model and reinitialize the new model LSTM model with same architecture and set batch size equal to 1.
# re-define the batch size
n_batch = 1
# re-define model
new_model = Sequential()
new_model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True))
new_model.add(Dense(1))
# copy weights
old_weights = model.get_weights()
new_model.set_weights(old_weights)
Now you can easily predict and train LSTMs with different batch sizes.
For more information please read: https://machinelearningmastery.com/use-different-batch-sizes-training-predicting-python-keras/
I found below helpful (and fully inline with above). The section "Solution 3: Copy Weights" worked for me:
How to use Different Batch Sizes when Training and Predicting with LSTMs, by Jason Brownlee
n_neurons = 10
# design network
model = Sequential()
model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
# fit network
for i in range(n_epoch):
model.fit(X, y, epochs=1, batch_size=n_batch, verbose=1, shuffle=False)
model.reset_states()
# re-define the batch size
n_batch = 1
# re-define model
new_model = Sequential()
new_model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True))
new_model.add(Dense(1))
# copy weights
old_weights = model.get_weights()
new_model.set_weights(old_weights)
# compile model
new_model.compile(loss='mean_squared_error', optimizer='adam')
I also have same problem and resolved it.
In another way, you can save your weights, when you test your result, you can reload your model with same architecture and set batch_size=1 as below:
n_neurons = 10
# design network
model = Sequential()
model.add(LSTM(n_neurons, batch_size=1, batch_input_shape=(n_batch,X.shape[1], X.shape[2]), statefull=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.load_weights("w.h5")
It will work well. I hope it will helpfull for you.
If you don't have access to the code that created the model or if you just don't want your prediction/validation code to depend on your model creation and training code there is another way:
You could create a new model from a modified version of the loaded model's config like this:
loaded_model = tf.keras.models.load_model('model_file.h5')
config = loaded_model.get_config()
old_batch_input_shape = config['layers'][0]['config']['batch_input_shape']
config['layers'][0]['config']['batch_input_shape'] = (new_batch_size, old_batch_input_shape[1])
new_model = loaded_model.__class__.from_config(config)
new_model.set_weights(loaded_model.get_weights())
This works well for me in a situation where I have several different models with state-full RNN layers working together in a graph network but being trained separately with different networks leading to different batch sizes. It allows me to experiment with the model structures and training batches without needing to change anything in my validation script.
I would like to modify the ImageNet caffe model as described bellow:
As the input channel number for temporal nets is different from that
of spatial nets (20 vs. 3), we average the ImageNet model filters of
first layer across the channel, and then copy the average results 20
times as the initialization of temporal nets.
My question is how can I achive the above results? How can I open the caffe model to be able to do those changes to it?
I read the net surgery tutorial but it doesn't cover the procedure needed.
Thank you for your assistance!
AMayer
The Net Surgery tutorial should give you the basics you need to cover this. But let me explain the steps you need to do in more detail:
Prepare the .prototxt network architectures: You need two files: the existing ImageNet .prototxt file, and your new temporal network architecture. You should make all layers except the first convolutional layers identical in both networks, including the names of the layers. That way, you can use the ImageNet .caffemodel file to initialize the weights automatically.
As the first conv layer has a different size, you have to give it a different name in your .prototxt file than it has in the ImageNet file. Otherwise, Caffe will try to initialize this layer with the existing weights too, which will fail as they have different shapes. (This is what happens in the edit to your question.) Just name it e.g. conv1b and change all references to that layer accordingly.
Load the ImageNet network for testing, so you can extract the parameters from the model file:
net = caffe.Net('imagenet.prototxt', 'imagenet.caffemodel', caffe.TEST)
Extract the weights from this loaded model.
conv_1_weights = old_net.params['conv1'][0].data
conv_1_biases = old_net.params['conv1'][1].data
Average the weights across the channels:
conv_av_weights = np.mean(conv_1_weights, axis=1, keepdims=True)
Load your new network together with the old .caffemodel file, as all layers except for the first layer directly use the weights from ImageNet:
new_net = caffe.Net('new_network.prototxt', 'imagenet.caffemodel', caffe.TEST)
Assign your calculated average weights to the new network
new_net.params['conv1b'][0].data[...] = conv_av_weights
new_net.params['conv1b'][1].data[...] = conv_1_biases
Save your weights to a new .caffemodel file:
new_net.save('new_weights.caffemodel')
I started with Caffe and the mnist example ran well.
I have the train and label data as data.mat. (I have 300 training data with 30 features and labels are (-1, +1) that have saved in data.mat).
However, I don't quite understand how I can use caffe to implement my own dataset?
Is there a step by step tutorial can teach me?
Many thanks!!!! Any advice would be appreciated!
I think the most straight forward way to transfer data from Matlab to caffe is via HDF5 file.
First, save your data in Matlab in an HDF5 file using hdf5write. I assume your training data is stored in a variable name X of size 300-by-30 and the labels are stored in y a 300-by-1 vector:
hdf5write('my_data.h5', '/X',
single( permute(reshape(X,[300, 30, 1, 1]),[4:-1:1]) ) );
hdf5write('my_data.h5', '/label',
single( permute(reshape(y,[300, 1, 1, 1]),[4:-1:1]) ),
'WriteMode', 'append' );
Note that the data is saved as a 4D array: the first dimension is the number of features, second one is the feature's dimension and the last two are 1 (representing no spatial dimensions). Also note that the names given to the data in the HDF5 are "X" and "label" - these names should be used as the "top" blobs of the input data layer.
Why permute? please see this answer for an explanation.
You also need to prepare a text file listing the names of all hdf5 files you are using (in your case, only my_data.h5). File /path/to/list/file.txt should have a single line
/path/to/my_data.h5
Now you can add an input data layer to your train_val.prototxt
layer {
type: "HDF5Data"
name: "data"
top: "X" # note: same name as in HDF5
top: "label" #
hdf5_data_param {
source: "/path/to/list/file.txt"
batch_size: 20
}
include { phase: TRAIN }
}
For more information regarding hdf5 input layer, you can see in this answer.
Is there a simple way of renaming layers in a caffe network by using the pycaffe interface?
I have looked through the net surgery example, but I cannot find an example of what I need.
For example, I would like to load a trained Caffe model and change the name of conv1 layer and its corresponding blob to new-conv1.
I don't know a direct way to do it, but here is a workaround:
Given a pretrained Caffe model my_model.caffemodel and its net architecture net.prototxt. Make a copy of net.prototxt (say net_new.prototxt), and change the name of conv1 layer to new-conv1 (you can change the names of bottom and top if you want).
import caffe
net_old = caffe.Net('net.prototxt','my_model.caffemodel',caffe.TEST)
net_new = caffe.Net('net_new.prototxt','my_model.caffemodel',caffe.TEST)
net_new.params['new-conv1'][0].data[...] = net_old.params['conv1'][0].data[...] #copy filter across 2 nets
net_new.params['new-conv1'][1].data[...] = net_old.params['conv1'][1].data[...] #copy bias
net_new.save('my_model_new.caffemodel')