Exemplo n.º 1
0
def test_min_informative_str():
    # evaluates a reference output to make sure the
    # min_informative_str function works as intended

    A = tensor.matrix(name="A")
    B = tensor.matrix(name="B")
    C = A + B
    C.name = "C"
    D = tensor.matrix(name="D")
    E = tensor.matrix(name="E")

    F = D + E
    G = C + F

    mis = min_informative_str(G).replace("\t", "        ")

    reference = """A. Elemwise{add,no_inplace}
 B. C
 C. Elemwise{add,no_inplace}
  D. D
  E. E"""

    if mis != reference:
        print("--" + mis + "--")
        print("--" + reference + "--")

    assert mis == reference
Exemplo n.º 2
0
def test_min_informative_str():
    """ evaluates a reference output to make sure the
        min_informative_str function works as intended """

    A = tensor.matrix(name='A')
    B = tensor.matrix(name='B')
    C = A + B
    C.name = 'C'
    D = tensor.matrix(name='D')
    E = tensor.matrix(name='E')

    F = D + E
    G = C + F

    mis = min_informative_str(G).replace("\t", "        ")

    reference = """A. Elemwise{add,no_inplace}
 B. C
 C. Elemwise{add,no_inplace}
  D. D
  E. E"""

    if mis != reference:
        print('--' + mis + '--')
        print('--' + reference + '--')

    assert mis == reference
Exemplo n.º 3
0
def test_min_informative_str():
    """ evaluates a reference output to make sure the
        min_informative_str function works as intended """

    A = tensor.matrix(name='A')
    B = tensor.matrix(name='B')
    C = A + B
    C.name = 'C'
    D = tensor.matrix(name='D')
    E = tensor.matrix(name='E')

    F = D + E
    G = C + F

    mis = min_informative_str(G).replace("\t", "        ")

    reference = """A. Elemwise{add,no_inplace}
 B. C
 C. Elemwise{add,no_inplace}
  D. D
  E. E"""

    if mis != reference:
        print('--' + mis + '--')
        print('--' + reference + '--')

    assert mis == reference
Exemplo n.º 4
0
def test_min_informative_str():
    """ evaluates a reference output to make sure the
        min_informative_str function works as intended """

    A = tensor.matrix(name="A")
    B = tensor.matrix(name="B")
    C = A + B
    C.name = "C"
    D = tensor.matrix(name="D")
    E = tensor.matrix(name="E")

    F = D + E
    G = C + F

    mis = min_informative_str(G).replace("\t", "        ")

    reference = """A. Elemwise{add,no_inplace}
        B. C
        C. Elemwise{add,no_inplace}
                D. D
                E. E"""

    if mis != reference:
        print "--" + mis + "--"
        print "--" + reference + "--"

    assert mis == reference
Exemplo n.º 5
0
        def handle_line(fgraph, line, i, node, fn):
            """
            Records new node computation.

            Parameters
            ----------
            line : string
                Line to record. For example, the function name or node name.
            i : integer
                Node number in the toposort order.
            node : Apply,
                The Apply node which created the entry.
            fn : Function,
                Function related to Apply node.
            """
            try:
                self.record.handle_line(line)
            except MismatchError as e:
                print("Got this MismatchError:")
                print(e)
                print(f"while processing node i={i}:")
                print(f"str(node):{node}")
                print("Symbolic inputs: ")
                for elem in node.inputs:
                    print(min_informative_str(elem))
                print("str(output) of outputs: ")
                for elem in fn.outputs:
                    assert isinstance(elem, list)
                    (elem, ) = elem
                    print(str(elem))
                print(f"function name: {fgraph.name}")
                raise MismatchError("Non-determinism detected by WrapLinker")
Exemplo n.º 6
0
        orig_obj = orig_obj[field[1:-1]]
    elif field.startswith('.'):
        obj_name += '.' + field
        orig_obj = getattr(orig_obj,field[1:])
    else:
        obj_name + '[' + field + ']'
        orig_obj = orig_obj[eval(field)]
    if id(orig_obj) in cycle_check:
        print "You're going in circles, "+obj_name+" is the same as "+cycle_check[id(orig_obj)]
        quit()
    cycle_check[id(orig_obj)] = obj_name


print 'type of object: '+str(type(orig_obj))
print 'object: '+str(orig_obj)
print 'object, longer description:\n'+min_informative_str(orig_obj, indent_level = 1)

t1 = time.time()
s = cPickle.dumps(orig_obj)
t2 = time.time()
prev_ts = t2 - t1

prev_bytes = len(s)
print 'orig_obj bytes: \t\t\t\t'+str(prev_bytes)
t1 = time.time()
x = cPickle.loads(s)
t2 = time.time()
prev_t = t2 - t1
print 'orig load time: '+str(prev_t)
print 'orig save time: '+str(prev_ts)
Exemplo n.º 7
0
nodes = func.maker.fgraph.toposort()

count = 0
for node in nodes:
    if str(type(node.op)).lower().find('hostfrom') != -1:
        count += 1
    found = 0
    for ipt in node.inputs:
        if ipt.owner is not None and str(type(ipt.owner.op)).lower().find('hostfrom') != -1:
            found += 1
            try:
                print ipt.ndim,'dimensions'
            except:
                print 'no ndm'
            print min_informative_str(ipt)
    if found > 0:
        print type(node.op), found
        try:
            print '\t',type(node.op.scalar_op)
        except:
            pass

print count


"""
i = 58
for key in mf1mod.hidden_layers[0].transformer.get_params():
    func = function([Xb,yb,alpha], updates[key], on_unused_input = 'ignore')
Exemplo n.º 8
0
nodes = func.maker.fgraph.toposort()

count = 0
for node in nodes:
    if str(type(node.op)).lower().find('hostfrom') != -1:
        count += 1
    found = 0
    for ipt in node.inputs:
        if ipt.owner is not None and str(type(
                ipt.owner.op)).lower().find('hostfrom') != -1:
            found += 1
            try:
                print ipt.ndim, 'dimensions'
            except:
                print 'no ndm'
            print min_informative_str(ipt)
    if found > 0:
        print type(node.op), found
        try:
            print '\t', type(node.op.scalar_op)
        except:
            pass

print count

test = CIFAR10(which_set='test', one_hot=True, gcn=55.)

yl = T.argmax(yb, axis=1)

mf1acc = 1. - T.neq(yl, T.argmax(ymf1, axis=1)).mean()
#mfnacc = 1.-T.neq(yl , T.argmax(mfny,axis=1)).mean()
Exemplo n.º 9
0
X = space.make_theano_batch()
X.tag.test_value = space.get_origin_batch(m).astype(X.dtype)

inputs = [X]

history = model.mf(X, return_history=True)
for elem in history:
    assert isinstance(elem, (list, tuple))
    assert len(elem) == len(model.hidden_layers)
outputs = [elem[-1] for elem in history]

for elem in outputs:
    for value in get_debug_values(elem):
        if value.shape[0] != m:
            print 'culprint is',id(elem)
            print min_informative_str(elem)
            quit(-1)

f = function(inputs, outputs)


n_classes = model.hidden_layers[-1].n_classes
if isinstance(n_classes, float):
    assert n_classes == int(n_classes)
    n_classes = int(n_classes)
assert isinstance(n_classes, int)
templates = np.zeros((n_classes, space.get_total_dimension()))
for i in xrange(n_classes):
    for j in xrange(-1, -dataset.X.shape[0], -1):
        if dataset.y[j,i]:
            templates[i, :] = dataset.X[j, :]
Exemplo n.º 10
0
        do_one_V_update()
        do_one_optimized_H_update()


cur_V = V
cur_H = H

for i in xrange(num_rounds):
    cur_V = update_V(cur_H)
    cur_H = update_H(cur_V)

print 'Compiling unrolled theano'
unrolled_theano = function([], updates={V: cur_V, H: cur_H})

from theano.printing import min_informative_str
print min_informative_str(unrolled_theano.maker.env.outputs[0])
assert False


def unrolled_loop():
    init()
    unrolled_theano()


print 'Timing python loop'
run_timed_trial(python_loop)

print 'Timing unrolled loop'
run_timed_trial(unrolled_loop)

print 'Timing optimized python loop'
Exemplo n.º 11
0
        elif field.startswith('.'):
            obj_name += '.' + field
            orig_obj = getattr(orig_obj, field[1:])
        else:
            obj_name + '[' + field + ']'
            orig_obj = orig_obj[eval(field)]
        if id(orig_obj) in cycle_check:
            print("You're going in circles, " + obj_name + " is the same as " +
                  cycle_check[id(orig_obj)])
            quit()
        cycle_check[id(orig_obj)] = obj_name

    print('type of object: ' + str(type(orig_obj)))
    print('object: ' + str(orig_obj))
    print('object, longer description:\n' +
          min_informative_str(orig_obj, indent_level=1))

    t1 = time.time()
    s = cPickle.dumps(orig_obj, hp)
    t2 = time.time()
    prev_ts = t2 - t1

    prev_bytes = len(s)
    print('orig_obj bytes: \t\t\t\t' + str(prev_bytes))
    t1 = time.time()
    x = cPickle.loads(s)
    t2 = time.time()
    prev_t = t2 - t1
    print('orig load time: ' + str(prev_t))
    print('orig save time: ' + str(prev_ts))
Exemplo n.º 12
0
    for i in xrange(num_rounds):
        do_one_V_update()
        do_one_optimized_H_update()

cur_V = V
cur_H = H

for i in xrange(num_rounds):
    cur_V = update_V(cur_H)
    cur_H = update_H(cur_V)

print 'Compiling unrolled theano'
unrolled_theano = function([], updates = { V : cur_V, H : cur_H } )

from theano.printing import min_informative_str
print min_informative_str(unrolled_theano.maker.env.outputs[0])
assert False

def unrolled_loop():
    init()
    unrolled_theano()


print 'Timing python loop'
run_timed_trial(python_loop)

print 'Timing unrolled loop'
run_timed_trial(unrolled_loop)

print 'Timing optimized python loop'
run_timed_trial(optimized_python_loop)
Exemplo n.º 13
0
X = space.make_theano_batch()
X.tag.test_value = space.get_origin_batch(m).astype(X.dtype)

inputs = [X]

history = model.mf(X, return_history=True)
for elem in history:
    assert isinstance(elem, (list, tuple))
    assert len(elem) == len(model.hidden_layers)
outputs = [elem[-1] for elem in history]

for elem in outputs:
    for value in get_debug_values(elem):
        if value.shape[0] != m:
            print 'culprint is', id(elem)
            print min_informative_str(elem)
            quit(-1)

f = function(inputs, outputs)

n_classes = model.hidden_layers[-1].n_classes
if isinstance(n_classes, float):
    assert n_classes == int(n_classes)
    n_classes = int(n_classes)
assert isinstance(n_classes, int)
templates = np.zeros((n_classes, space.get_total_dimension()))
for i in xrange(n_classes):
    for j in xrange(-1, -dataset.X.shape[0], -1):
        if dataset.y[j, i]:
            templates[i, :] = dataset.X[j, :]