def train_mnist(args):
# write tensorflow models
x = tf.placeholder(tf.float32, [args.batch_size, 784])
t = tf.placeholder(tf.float32, [args.batch_size, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, w) + b
cost = tf.reduce_mean(
-tf.reduce_sum(t * tf.log(tf.nn.softmax(y)), reduction_indices=[1]))
init = tf.initialize_all_variables()
# import graph_def
with tf.Session() as sess:
graph_def = sess.graph_def
importer = TFImporter()
importer.parse_graph_def(graph_def)
# get handle of ngraph ops
x_ng, t_ng, cost_ng, init_op_ng = importer.get_op_handle(
[x, t, cost, init])
# transformer and computations
transformer = ngt.make_transformer()
updates = SGDOptimizer(args.lrate).minimize(cost_ng)
train_comp = transformer.computation([cost_ng, updates], x_ng, t_ng)
init_comp = transformer.computation(init_op_ng)
transformer.initialize()
# train
mnist = input_data.read_data_sets(args.data_dir, one_hot=True)
init_comp()
for idx in range(args.max_iter):
batch_xs, batch_ys = mnist.train.next_batch(args.batch_size)
cost_val, _ = train_comp(batch_xs, batch_ys)
print("[Iter %s] Cost = %s" % (idx, cost_val))
# train in tensorflow as comparison
with tf.Session() as sess:
# train in tensorflow
train_step = tf.train.GradientDescentOptimizer(
args.lrate).minimize(cost)
sess.run(init)
mnist = input_data.read_data_sets(args.data_dir, one_hot=True)
for idx in range(args.max_iter):
batch_xs, batch_ys = mnist.train.next_batch(args.batch_size)
cost_val, _ = sess.run([cost, train_step],
feed_dict={
x: batch_xs,
t: batch_ys
})
print("[Iter %s] Cost = %s" % (idx, cost_val))
def test_mnist_softmax(self):
# parameters
max_iter = 10
lrate = 0.1
bsz = 128
# write tensorflow models
x = tf.placeholder(tf.float32, [bsz, 784])
t = tf.placeholder(tf.float32, [bsz, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b
cost = tf.reduce_mean(-tf.reduce_sum(
t * tf.log(tf.nn.softmax(y)), reduction_indices=[1]))
init_op = tf.initialize_all_variables()
# import graph_def
with tf.Session() as sess:
graph_def = sess.graph_def
importer = TFImporter()
importer.parse_graph_def(graph_def)
# get handle of ngraph ops
x_ng, t_ng, cost_ng, init_op_ng = importer.get_op_handle(
[x, t, cost, init_op])
# transformer and computations
transformer = ngt.make_transformer()
updates = SGDOptimizer(lrate).minimize(cost_ng)
train_comp = transformer.computation([cost_ng, updates], x_ng, t_ng)
init_comp = transformer.computation(init_op_ng)
transformer.initialize()
# train
mnist = input_data.read_data_sets('/tmp/data', one_hot=True)
init_comp()
ng_costs = []
for idx in range(max_iter):
batch_xs, batch_ys = mnist.train.next_batch(bsz)
cost_val, _ = train_comp(batch_xs, batch_ys)
print("[Iter %s] Cost = %s" % (idx, cost_val))
ng_costs.append(float(cost_val))
# train in tensorflow as comparison
with tf.Session() as sess:
# train in tensorflow
train_step = tf.train.GradientDescentOptimizer(lrate).minimize(
cost)
sess.run(init_op)
mnist = input_data.read_data_sets('/tmp/data', one_hot=True)
tf_costs = []
for idx in range(max_iter):
batch_xs, batch_ys = mnist.train.next_batch(bsz)
cost_val, _ = sess.run([cost, train_step],
feed_dict={x: batch_xs,
t: batch_ys})
print("[Iter %s] Cost = %s" % (idx, cost_val))
tf_costs.append(cost_val)
# check results
assert np.allclose(
np.asarray(tf_costs).astype(np.float32),
np.asarray(ng_costs).astype(np.float32))
lrate = 0.1
# placeholders
x = tf.placeholder(tf.float32, shape=(4, 3))
t = tf.placeholder(tf.float32, shape=(4, 1))
w = tf.Variable(tf.zeros([3, 1]))
y = tf.nn.sigmoid(tf.matmul(x, w))
log_likelihoods = tf.log(y) * t + tf.log(1 - y) * (1 - t)
cost = -tf.reduce_sum(log_likelihoods)
init_op = tf.initialize_all_variables()
# import graph_def
with tf.Session() as sess:
graph_def = sess.graph_def
importer = TFImporter()
importer.parse_graph_def(graph_def)
# get handle of ngraph ops
x_ng, t_ng, cost_ng, init_op_ng = importer.get_op_handle([x, t, cost, init_op])
# transformer and computations
transformer = ngt.make_transformer()
updates = SGDOptimizer(lrate).minimize(cost_ng)
train_comp = transformer.computation([cost_ng, updates], x_ng, t_ng)
init_comp = transformer.computation(init_op_ng)
transformer.initialize()
# train
init_comp()
for idx in range(max_iter):
loss_val, _ = train_comp(xs_np, ys_np)
# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ---------------------------------------------------------------------------- from __future__ import print_function from ngraph.frontends.tensorflow.tf_importer.importer import TFImporter import tensorflow as tf import ngraph.transformers as ngt # tensorflow ops x = tf.constant(1.) y = tf.constant(2.) f = x + y # import importer = TFImporter() importer.parse_graph_def(tf.Session().graph_def) # get handle f_ng = importer.get_op_handle(f) # execute f_result = ngt.make_transformer().computation(f_ng)() print(f_result)