update_learning_rate=0.1,
update_momentum=0.9,
max_epochs=500,
verbose=1,
)
net1.fit(X_training_Matrix,Y_training_Matrix)
logReg = linear_model.LogisticRegression(C=1e6)
logReg.fit(X_training_Matrix, Y_training_Matrix)
dbn = dbn.DBN(
[X_training.shape[1],1000,2],
learn_rates = 0.3,
learn_rate_decays = 0.9,
epochs = 10,
verbose = 1)
dbn.fit(X_training, Y_training)
net1Preds,net1Probs = net1.predict(X_test_Matrix)
dbnPreds,dbnProbs = dbn.predict(X_test)
logRegPreds = logReg.predict(X_test_Matrix)
print "neural network"
print classification_report(Y_test_Matrix, net1Preds)
print "deep belief network"
print classification_report(Y_test, dbnPreds)
print "logistic regression"
print classification_report(Y_test_Matrix, logRegPreds)
count = 0
i = 0
while i < 237:
max_epochs=100,
verbose=1,
)
net1.fit(videoFeatureTraining, Y_training_Matrix)
videoFeaturenet1Preds,videoFeaturenet1Probs = net1.predict(videoFeatureTest)
print "neural network using video features"
print classification_report(Y_test_Matrix,videoFeaturenet1Preds)
#########################################################################################################
##################### THIS IS NEURAL NETWORK #######################################
"""
##################### THIS IS DEEP BELIEF NETWORK #######################################
"""
########################deep belief network with video features###########################################
videoFeatureTraining = X_training_Matrix[0:200,35:]
videoFeatureTest = X_test_Matrix[0:237,35:]
dbn = dbn.DBN(
[videoFeatureTraining.shape[1],1000,2],
learn_rates = 0.3,
learn_rate_decays = 0.9,
epochs = 10,
verbose = 1)
dbn.fit(videoFeatureTraining, Y_training_Matrix)
videoFeatureDBNPreds,videoFeatureDBNProbs = dbn.predict(videoFeatureTest)
print classification_report(Y_test_Matrix,videoFeatureDBNPreds)
########################deep belief network with video features###########################################
"""
"""
