def main():
# The tests can't even run if nose isn't available, so might as well give the
# user a civilized error message in that case.
try:
import nose
except ImportError:
error = """\
ERROR: The IPython test suite requires nose to run.
Please install nose on your system first and try again.
For information on installing nose, see:
http://nose.readthedocs.org/en/latest/
Exiting."""
import sys
print(error, file=sys.stderr)
else:
import theano
theano.test()
def main():
# The tests can't even run if nose isn't available, so might as well give the
# user a civilized error message in that case.
try:
import nose
except ImportError:
error = """\
ERROR: The IPython test suite requires nose to run.
Please install nose on your system first and try again.
For information on installing nose, see:
http://nose.readthedocs.org/en/latest/
Exiting."""
import sys
print(error, file=sys.stderr)
else:
import theano
theano.test()
__author__ = 'rguo12' import theano theano.test()
# # print (str(w.get_value()) + str(b.get_value()))
# # Construct Theano expression graph
# p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b)) # Probability that target = 1
# prediction = p_1 > 0.5 # The prediction thresholded
# xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function
# cost = xent.mean() + 0.01 * (w ** 2).sum()# The cost to minimize
# gw, gb = T.grad(cost, [w, b]) # Compute the gradient of the cost
# # (we shall return to this in a
# # following section of this tutorial)
# # Compile
# train = theano.function(
# inputs=[x,y],
# outputs=[prediction, xent],
# updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)))
# predict = theano.function(inputs=[x], outputs=prediction)
# # Train
# for i in range(training_steps):
# pred, err = train(D[0], D[1])
# print ("Final model:")
# print (str(w.get_value()) + str(b.get_value()))
# print ("target values for D:"+ D[1])
# print ("prediction on D:"+ predict(D[0]))
import theano
theano.test()
# print (theano.config)