Mercurial > ift6266
changeset 212:e390b0454515
added classic lr time decay and py code to calculate the error based on a saved model
| author | xaviermuller |
|---|---|
| date | Wed, 10 Mar 2010 16:17:59 -0500 |
| parents | 476da2ba6a12 |
| children | a96fa4de06d2 c89004f9cab2 |
| files | baseline/mlp/mlp_get_error_from_model.py baseline/mlp/mlp_nist.py |
| diffstat | 2 files changed, 141 insertions(+), 2 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/baseline/mlp/mlp_get_error_from_model.py Wed Mar 10 16:17:59 2010 -0500 @@ -0,0 +1,124 @@ +__docformat__ = 'restructedtext en' + +import pdb +import numpy as np +import pylab +import time +import pylearn +from pylearn.io import filetensor as ft + +data_path = '/data/lisa/data/nist/by_class/' +test_data = 'all/all_test_data.ft' +test_labels = 'all/all_test_labels.ft' + +def read_test_data(mlp_model): + + + #read the data + h = open(data_path+test_data) + i= open(data_path+test_labels) + raw_test_data = ft.read(h) + raw_test_labels = ft.read(i) + i.close() + h.close() + + #read the model chosen + a=np.load(mlp_model) + W1=a['W1'] + W2=a['W2'] + b1=a['b1'] + b2=a['b2'] + + return (W1,b1,W2,b2,raw_test_data,raw_test_labels) + + + + +def get_total_test_error(everything): + + W1=everything[0] + b1=everything[1] + W2=everything[2] + b2=everything[3] + test_data=everything[4]/255.0 + test_labels=everything[5] + total_error_count=0 + total_exemple_count=0 + + nb_error_count=0 + nb_exemple_count=0 + + char_error_count=0 + char_exemple_count=0 + + min_error_count=0 + min_exemple_count=0 + + maj_error_count=0 + maj_exemple_count=0 + + for i in range(test_labels.size): + total_exemple_count = total_exemple_count +1 + #get activation for layer 1 + a0=np.dot(np.transpose(W1),np.transpose(test_data[i])) + b1 + #add non linear function to layer 1 activation + a0_out=np.tanh(a0) + + #get activation for output layer + a1= np.dot(np.transpose(W2),a0_out) + b2 + #add non linear function for output activation (softmax) + a1_exp = np.exp(a1) + sum_a1=np.sum(a1_exp) + a1_out=a1_exp/sum_a1 + + predicted_class=np.argmax(a1_out) + wanted_class=test_labels[i] + + if(predicted_class!=wanted_class): + total_error_count = total_error_count +1 + + #get grouped based error + if(wanted_class>9 and wanted_class<35): + min_exemple_count=min_exemple_count+1 + predicted_class=np.argmax(a1_out) + if(predicted_class!=wanted_class): + min_error_count=min_error_count+1 + elif(wanted_class<10): + nb_exemple_count=nb_exemple_count+1 + predicted_class=np.argmax(a1_out) + if(predicted_class!=wanted_class): + nb_error_count=nb_error_count+1 + elif(wanted_class>34): + maj_exemple_count=maj_exemple_count+1 + predicted_class=np.argmax(a1_out) + if(predicted_class!=wanted_class): + maj_error_count=maj_error_count+1 + + if(wanted_class>9): + char_exemple_count=char_exemple_count+1 + predicted_class=np.argmax(a1_out) + if(predicted_class!=wanted_class): + char_error_count=char_error_count+1 + + + #convert to float + return ( total_exemple_count,nb_exemple_count,char_exemple_count,min_exemple_count,maj_exemple_count,\ + total_error_count,nb_error_count,char_error_count,min_error_count,maj_error_count,\ + total_error_count*100.0/total_exemple_count*1.0,\ + nb_error_count*100.0/nb_exemple_count*1.0,\ + char_error_count*100.0/char_exemple_count*1.0,\ + min_error_count*100.0/min_exemple_count*1.0,\ + maj_error_count*100.0/maj_exemple_count*1.0) + + + + + + + + + + + + + \ No newline at end of file
--- a/baseline/mlp/mlp_nist.py Wed Mar 10 09:56:02 2010 -0500 +++ b/baseline/mlp/mlp_nist.py Wed Mar 10 16:17:59 2010 -0500 @@ -31,6 +31,7 @@ import time import theano.tensor.nnet import pylearn +import theano,pylearn.version from pylearn.io import filetensor as ft data_path = '/data/lisa/data/nist/by_class/' @@ -174,17 +175,22 @@ nb_max_exemples=1000000,\ batch_size=20,\ nb_hidden = 500,\ - nb_targets = 62): + nb_targets = 62, + tau=1e6): configuration = [learning_rate,nb_max_exemples,nb_hidden,adaptive_lr] + #save initial learning rate if classical adaptive lr is used + initial_lr=learning_rate + total_validation_error_list = [] total_train_error_list = [] learning_rate_list=[] best_training_error=float('inf'); + f = open(data_path+train_data) g= open(data_path+train_labels) @@ -315,6 +321,8 @@ n_iter = nb_max_exemples/batch_size # nb of max times we are allowed to run through all exemples n_iter = n_iter/n_minibatches + 1 #round up n_iter=max(1,n_iter) # run at least once on short debug call + time_n=0 #in unit of exemples + if verbose == True: @@ -325,6 +333,9 @@ epoch = iter / n_minibatches minibatch_index = iter % n_minibatches + + if adaptive_lr==2: + classifier.lr.value = tau*initial_lr/(tau+time_n) # get the minibatches corresponding to `iter` modulo @@ -364,6 +375,8 @@ print('epoch %i, minibatch %i/%i, validation error %f, training error %f %%' % \ (epoch, minibatch_index+1, n_minibatches, \ this_validation_loss*100.,this_train_loss*100)) + print 'learning rate = %f' %classifier.lr.value + print 'time = %i' %time_n #save the learning rate @@ -425,6 +438,7 @@ break + time_n= time_n + batch_size end_time = time.clock() if verbose == True: print(('Optimization complete. Best validation score of %f %% ' @@ -448,7 +462,8 @@ (train_error,validation_error,test_error,nb_exemples,time)=mlp_full_nist(learning_rate=state.learning_rate,\ nb_max_exemples=state.nb_max_exemples,\ nb_hidden=state.nb_hidden,\ - adaptive_lr=state.adaptive_lr) + adaptive_lr=state.adaptive_lr,\ + tau=tau) state.train_error=train_error state.validation_error=validation_error state.test_error=test_error