def validate(data_type, model, saved_model, class_limit, image_shape=None):
batch_size = 32
print "Got here!"
seq_length = 40
# Get the data and process it.
if image_shape is None:
data = DataSet(seq_length=seq_length, class_limit=class_limit)
else:
data = DataSet(seq_length=seq_length,
class_limit=class_limit,
image_shape=image_shape)
X, y, Z = data.get_all_sequences_in_memory_names('test', 'features')
print "X len: " + str(len(X))
print "y len: " + str(len(y))
print "Z len: " + str(len(Z))
names = []
# Process names
for row in Z:
foo = row[2].split("_")
foo.pop(0)
names.append(foo)
print names
# Get the model.
# rm = ResearchModels(len(data.classes), model, seq_length, saved_model)
rm = ResearchModels(3, model, seq_length, saved_model)
print "Beggining prediction"
a = rm.model.predict(X, verbose=1)
b = np.zeros_like(a)
b[np.arange(len(a)), a.argmax(1)] = 1
class_result = b
print "\t result: " + str(class_result)
print "\t total result: " + str(np.sum(b, axis=0))
# now combine arrays
total = np.hstack((names, b))
print total
# save as csv
with open('foo.csv', 'w') as output:
writer = csv.writer(output, lineterminator='\n')
writer.writerow([
"device", "trip", "starttime", "endttime", "Flashing",
"Not_Flashing", "Unknown"
])
writer.writerows(total)
"""
# Write results to csv file
myFile = open("first_output_1.csv",'w')
with myFile
writer = csv.writer(myFile)
for
names, preds = [],[]
for i in range(len(X)):
x = X[i]
print("\t x = " + str(x))
name = Z[i]
pred = rm.model.predict(x, batch_size = 1)
print "\t Predicted class: " + str(pred) + " for name: " + str(name)
names.append(name)
preds.append(pred)
"""
print "\t\t END"
