def testNumpyInputFnWithMismatchLengthOfInputs(self):
a = np.arange(4) * 1.0
b = np.arange(32, 36)
x = {'a': a, 'b': b}
x_mismatch_length = {'a': np.arange(1), 'b': b}
y_longer_length = np.arange(10)
with self.test_session():
with self.assertRaisesRegexp(
ValueError, 'Length of tensors in x and y is mismatched.'):
failing_input_fn = numpy_io.numpy_input_fn(x,
y_longer_length,
batch_size=2,
shuffle=False,
num_epochs=1)
failing_input_fn()
with self.assertRaisesRegexp(
ValueError, 'Length of tensors in x and y is mismatched.'):
failing_input_fn = numpy_io.numpy_input_fn(x=x_mismatch_length,
y=None,
batch_size=2,
shuffle=False,
num_epochs=1)
failing_input_fn()
def testNumpyInputFn(self):
a = np.arange(4) * 1.0
b = np.arange(32, 36)
x = {'a': a, 'b': b}
y = np.arange(-32, -28)
with self.test_session() as session:
input_fn = numpy_io.numpy_input_fn(
x, y, batch_size=2, shuffle=False, num_epochs=1)
features, target = input_fn()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(session, coord=coord)
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [0, 1])
self.assertAllEqual(res[0]['b'], [32, 33])
self.assertAllEqual(res[1], [-32, -31])
session.run([features, target])
with self.assertRaises(errors.OutOfRangeError):
session.run([features, target])
coord.request_stop()
coord.join(threads)
def testNumpyInputFnWithVeryLargeBatchSizeAndMultipleEpochs(self):
a = np.arange(2) * 1.0
b = np.arange(32, 34)
x = {'a': a, 'b': b}
y = np.arange(-32, -30)
with self.test_session() as session:
input_fn = numpy_io.numpy_input_fn(x,
y,
batch_size=128,
shuffle=False,
num_epochs=2)
features, target = input_fn()
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(session,
coord=coord)
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [0, 1, 0, 1])
self.assertAllEqual(res[0]['b'], [32, 33, 32, 33])
self.assertAllEqual(res[1], [-32, -31, -32, -31])
with self.assertRaises(errors.OutOfRangeError):
session.run([features, target])
coord.request_stop()
coord.join(threads)
def testNumpyInputFnWithDifferentDimensionsOfFeatures(self):
a = np.array([[1, 2], [3, 4]])
b = np.array([5, 6])
x = {'a': a, 'b': b}
y = np.arange(-32, -30)
with self.test_session() as session:
input_fn = numpy_io.numpy_input_fn(x,
y,
batch_size=2,
shuffle=False,
num_epochs=1)
features, target = input_fn()
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(session,
coord=coord)
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [[1, 2], [3, 4]])
self.assertAllEqual(res[0]['b'], [5, 6])
self.assertAllEqual(res[1], [-32, -31])
coord.request_stop()
coord.join(threads)
def testNumpyInputFn(self):
a = np.arange(4) * 1.0
b = np.arange(32, 36)
x = {'a': a, 'b': b}
y = np.arange(-32, -28)
with self.test_session() as session:
input_fn = numpy_io.numpy_input_fn(x,
y,
batch_size=2,
shuffle=False,
num_epochs=1)
features, target = input_fn()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(session, coord=coord)
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [0, 1])
self.assertAllEqual(res[0]['b'], [32, 33])
self.assertAllEqual(res[1], [-32, -31])
session.run([features, target])
with self.assertRaises(errors.OutOfRangeError):
session.run([features, target])
coord.request_stop()
coord.join(threads)
def testNumpyInputFnWithBatchSizeLargerThanDataSize(self):
batch_size = 10
a = np.arange(4) * 1.0
b = np.arange(32, 36)
x = {'a': a, 'b': b}
y = np.arange(-32, -28)
with self.test_session() as session:
input_fn = numpy_io.numpy_input_fn(x,
y,
batch_size=batch_size,
shuffle=False,
num_epochs=1)
features, target = input_fn()
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(session,
coord=coord)
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [0, 1, 2, 3])
self.assertAllEqual(res[0]['b'], [32, 33, 34, 35])
self.assertAllEqual(res[1], [-32, -31, -30, -29])
with self.assertRaises(errors.OutOfRangeError):
session.run([features, target])
coord.request_stop()
coord.join(threads)
def testNumpyInputFnWithBatchSizeNotDividedByDataSize(self):
batch_size = 2
a = np.arange(5) * 1.0
b = np.arange(32, 37)
x = {'a': a, 'b': b}
y = np.arange(-32, -27)
with self.test_session() as session:
input_fn = numpy_io.numpy_input_fn(
x, y, batch_size=batch_size, shuffle=False, num_epochs=1)
features, target = input_fn()
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(session, coord=coord)
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [0, 1])
self.assertAllEqual(res[0]['b'], [32, 33])
self.assertAllEqual(res[1], [-32, -31])
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [2, 3])
self.assertAllEqual(res[0]['b'], [34, 35])
self.assertAllEqual(res[1], [-30, -29])
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [4])
self.assertAllEqual(res[0]['b'], [36])
self.assertAllEqual(res[1], [-28])
with self.assertRaises(errors.OutOfRangeError):
session.run([features, target])
coord.request_stop()
coord.join(threads)
def testNumpyInputFnWithXAsNonDict(self):
x = np.arange(32, 36)
y = np.arange(4)
with self.test_session():
with self.assertRaisesRegexp(TypeError, 'x must be dict'):
failing_input_fn = numpy_io.numpy_input_fn(
x, y, batch_size=2, shuffle=False, num_epochs=1)
failing_input_fn()
def testNumpyInputFnWithXAsNonDict(self):
x = np.arange(32, 36)
y = np.arange(4)
with self.test_session():
with self.assertRaisesRegexp(TypeError, 'x must be dict'):
failing_input_fn = numpy_io.numpy_input_fn(
x, y, batch_size=2, shuffle=False, num_epochs=1)
failing_input_fn()
def testNumpyInputFnWithTargetKeyAlreadyInX(self):
array = np.arange(32, 36)
x = {'__target_key__': array}
y = np.arange(4)
with self.test_session():
input_fn = numpy_io.numpy_input_fn(
x, y, batch_size=2, shuffle=False, num_epochs=1)
input_fn()
self.assertAllEqual(x['__target_key__'], array)
self.assertAllEqual(x['__target_key___n'], y)
def testNumpyInputFnWithTargetKeyAlreadyInX(self):
array = np.arange(32, 36)
x = {'__target_key__': array}
y = np.arange(4)
with self.test_session():
input_fn = numpy_io.numpy_input_fn(
x, y, batch_size=2, shuffle=False, num_epochs=1)
input_fn()
self.assertAllEqual(x['__target_key__'], array)
self.assertAllEqual(x['__target_key___n'], y)
def testNumpyInputFnWithMismatchLengthOfInputs(self):
a = np.arange(4) * 1.0
b = np.arange(32, 36)
x = {'a': a, 'b': b}
x_mismatch_length = {'a': np.arange(1), 'b': b}
y_longer_length = np.arange(10)
with self.test_session():
with self.assertRaisesRegexp(ValueError, 'Shape of x and y are mismatch'):
failing_input_fn = numpy_io.numpy_input_fn(
x, y_longer_length, batch_size=2, shuffle=False, num_epochs=1)
failing_input_fn()
with self.assertRaisesRegexp(ValueError, 'Shape of x and y are mismatch'):
failing_input_fn = numpy_io.numpy_input_fn(
x=x_mismatch_length,
y=None,
batch_size=2,
shuffle=False,
num_epochs=1)
failing_input_fn()
def testNumpyInputFnWithBatchSizeNotDividedByDataSizeAndMultipleEpochs(
self):
batch_size = 2
a = np.arange(3) * 1.0
b = np.arange(32, 35)
x = {'a': a, 'b': b}
y = np.arange(-32, -29)
with self.test_session() as session:
input_fn = numpy_io.numpy_input_fn(x,
y,
batch_size=batch_size,
shuffle=False,
num_epochs=3)
features, target = input_fn()
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(session,
coord=coord)
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [0, 1])
self.assertAllEqual(res[0]['b'], [32, 33])
self.assertAllEqual(res[1], [-32, -31])
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [2, 0])
self.assertAllEqual(res[0]['b'], [34, 32])
self.assertAllEqual(res[1], [-30, -32])
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [1, 2])
self.assertAllEqual(res[0]['b'], [33, 34])
self.assertAllEqual(res[1], [-31, -30])
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [0, 1])
self.assertAllEqual(res[0]['b'], [32, 33])
self.assertAllEqual(res[1], [-32, -31])
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [2])
self.assertAllEqual(res[0]['b'], [34])
self.assertAllEqual(res[1], [-30])
with self.assertRaises(errors.OutOfRangeError):
session.run([features, target])
coord.request_stop()
coord.join(threads)
def testNumpyInputFnWithZeroEpochs(self):
a = np.arange(4) * 1.0
b = np.arange(32, 36)
x = {'a': a, 'b': b}
y = np.arange(-32, -28)
with self.test_session() as session:
input_fn = numpy_io.numpy_input_fn(
x, y, batch_size=2, shuffle=False, num_epochs=0)
features, target = input_fn()
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(session, coord=coord)
with self.assertRaises(errors.OutOfRangeError):
session.run([features, target])
coord.request_stop()
coord.join(threads)
def testNumpyInputFnWithDifferentDimensionsOfFeatures(self):
a = np.array([[1, 2], [3, 4]])
b = np.array([5, 6])
x = {'a': a, 'b': b}
y = np.arange(-32, -30)
with self.test_session() as session:
input_fn = numpy_io.numpy_input_fn(
x, y, batch_size=2, shuffle=False, num_epochs=1)
features, target = input_fn()
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(session, coord=coord)
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [[1, 2], [3, 4]])
self.assertAllEqual(res[0]['b'], [5, 6])
self.assertAllEqual(res[1], [-32, -31])
coord.request_stop()
coord.join(threads)
def testNumpyInputFnWithVeryLargeBatchSizeAndMultipleEpochs(self):
a = np.arange(2) * 1.0
b = np.arange(32, 34)
x = {'a': a, 'b': b}
y = np.arange(-32, -30)
with self.test_session() as session:
input_fn = numpy_io.numpy_input_fn(
x, y, batch_size=128, shuffle=False, num_epochs=2)
features, target = input_fn()
coord = coordinator.Coordinator()
threads = queue_runner_impl.start_queue_runners(session, coord=coord)
res = session.run([features, target])
self.assertAllEqual(res[0]['a'], [0, 1, 0, 1])
self.assertAllEqual(res[0]['b'], [32, 33, 32, 33])
self.assertAllEqual(res[1], [-32, -31, -32, -31])
with self.assertRaises(errors.OutOfRangeError):
session.run([features, target])
coord.request_stop()
coord.join(threads)
optimizer = tf.train.GradientDescentOptimizer(1e-2).minimize(loss)
global_step = tf.contrib.framework.get_global_step()
train_op = tf.group(optimizer, tf.assign_add(global_step, 1))
return tcb.learn.ModelFnOps(mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op)
# In[57]:
input_fn = numpy_input_fn(x={
"X": X1,
"Y": Y1
},
y=Z1,
batch_size=500,
num_epochs=10,
shuffle=False)
# In[59]:
estimator = tcb.learn.Estimator(model_fn=model_fn,
params={'shift': 0},
model_dir="/tmp/v4")
# In[60]:
estimator.fit(input_fn=input_fn)
# In[61]:
