def testTensorArrayReadWrongIndexOrDataTypeFails(self):
with self.test_session(use_gpu=self._use_gpu):
ta = tensor_array_ops.TensorArray(
dtype=tf.float32, tensor_array_name="foo", size=3)
w0 = ta.write(0, [[4.0, 5.0]])
# Test reading wrong datatype
r0_bad = gen_data_flow_ops._tensor_array_read(
handle=w0.handle, index=0, dtype=tf.int64, flow_in=w0.flow)
with self.assertRaisesOpError(
"TensorArray dtype is float but Op requested dtype int64."):
r0_bad.eval()
# Test reading from a different index than the one we wrote to
r1 = w0.read(1)
with self.assertRaisesOpError(
"Could not read from TensorArray index 1 because "
"it has not yet been written to."):
r1.eval()
# Test reading from a negative index
with self.assertRaisesOpError(
r"Tried to read from index -1 but array size is: 3"):
ta.read(-1).eval()
# Test reading from too large an index
with self.assertRaisesOpError(
"Tried to read from index 3 but array size is: 3"):
ta.read(3).eval()
def read(self, index):
"""Read the value at location `index` in the TensorArray."""
value = gen_data_flow_ops._tensor_array_read(handle=self._handle,
index=index,
flow_in=self._flow,
dtype=self._dtype)
return value
def testTensorArrayReadWrongIndexOrDataTypeFails(self):
with self.test_session(use_gpu=self._use_gpu):
ta = tensor_array_ops.TensorArray(
dtype=tf.float32, tensor_array_name="foo", size=3)
w0 = ta.write(0, [[4.0, 5.0]])
# Test reading wrong datatype
r0_bad = gen_data_flow_ops._tensor_array_read(
handle=w0.handle, index=0, dtype=tf.int64, flow_in=w0.flow)
with self.assertRaisesOpError(
"TensorArray dtype is float but Op requested dtype int64."):
r0_bad.eval()
# Test reading from a different index than the one we wrote to
r1 = w0.read(1)
with self.assertRaisesOpError(
"Could not read from TensorArray index 1 because "
"it has not yet been written to."):
r1.eval()
# Test reading from a negative index
with self.assertRaisesOpError(
r"Tried to read from index -1 but array size is: 3"):
ta.read(-1).eval()
# Test reading from too large an index
with self.assertRaisesOpError(
"Tried to read from index 3 but array size is: 3"):
ta.read(3).eval()
def _testMultiTensorArray(self, use_gpu):
with self.test_session(use_gpu=use_gpu):
h1 = gen_data_flow_ops._tensor_array(
size=1, dtype=tf.float32, tensor_array_name="foo")
with tf.control_dependencies([
gen_data_flow_ops._tensor_array_write(h1, 0, 4.0)]):
r1 = gen_data_flow_ops._tensor_array_read(h1, 0, tf.float32)
h2 = gen_data_flow_ops._tensor_array(
size=1, dtype=tf.float32, tensor_array_name="bar")
with tf.control_dependencies([
gen_data_flow_ops._tensor_array_write(h2, 0, 5.0)]):
r2 = gen_data_flow_ops._tensor_array_read(h2, 0, tf.float32)
r = r1 + r2
self.assertAllClose(9.0, r.eval())
def _testTensorArrayWriteRead(self, use_gpu):
with self.test_session(use_gpu=use_gpu) as sess:
h = gen_data_flow_ops._tensor_array(
dtype=tf.float32, tensor_array_name="foo", size=3)
writes = [
gen_data_flow_ops._tensor_array_write(h, 0, [[4.0, 5.0]]),
gen_data_flow_ops._tensor_array_write(h, 1, [[1.0]]),
gen_data_flow_ops._tensor_array_write(h, 2, -3.0)]
with tf.control_dependencies(writes):
r0 = gen_data_flow_ops._tensor_array_read(h, 0, tf.float32)
r1 = gen_data_flow_ops._tensor_array_read(h, 1, tf.float32)
r2 = gen_data_flow_ops._tensor_array_read(h, 2, tf.float32)
d0, d1, d2 = sess.run([r0, r1, r2])
self.assertAllEqual([[4.0, 5.0]], d0)
self.assertAllEqual([[1.0]], d1)
self.assertAllEqual(-3.0, d2)
def _testTensorArrayReadWrongIndexOrDataTypeFails(self, use_gpu):
with self.test_session(use_gpu=use_gpu):
h = gen_data_flow_ops._tensor_array(
dtype=tf.float32, tensor_array_name="foo", size=3)
with tf.control_dependencies([
gen_data_flow_ops._tensor_array_write(h, 0, [[4.0, 5.0]])]):
# Test reading wrong datatype
r0_bad = gen_data_flow_ops._tensor_array_read(h, 0, tf.int64)
with self.assertRaisesOpError(
"TensorArray dtype is float but Op requested dtype int64."):
r0_bad.eval()
# Test reading from a different index than the one we wrote to
r1 = gen_data_flow_ops._tensor_array_read(h, 1, tf.float32)
with self.assertRaisesOpError(
"Could not read from TensorArray index 1 because "
"it has not yet been written to."):
r1.eval()
# Test reading from a negative index
with self.assertRaisesOpError(
"Tried to read from index -1 but array size is: 3"):
gen_data_flow_ops._tensor_array_read(h, -1, tf.float32).eval()
# Test reading from too large an index
with self.assertRaisesOpError(
"Tried to read from index 3 but array size is: 3"):
gen_data_flow_ops._tensor_array_read(h, 3, tf.float32).eval()
def read(self, index, name=None):
"""Read the value at location `index` in the TensorArray.
Args:
index: 0-D. int32 tensor with the index to read from.
name: A name for the operation (optional).
Returns:
The tensor at index `index`.
"""
with ops.colocate_with(self._handle):
value = gen_data_flow_ops._tensor_array_read(
handle=self._handle, index=index, flow_in=self._flow, dtype=self._dtype, name=name
)
return value
def _testTensorGradAccessTwiceReceiveSameObject(self, use_gpu):
with self.test_session(use_gpu=use_gpu) as sess:
h = gen_data_flow_ops._tensor_array(
dtype=tf.float32, tensor_array_name="foo", size=3)
g_h_0 = gen_data_flow_ops._tensor_array_grad(h)
g_h_1 = gen_data_flow_ops._tensor_array_grad(h)
with tf.control_dependencies([
gen_data_flow_ops._tensor_array_write(g_h_0, 0, [[4.0, 5.0]])]):
# Write with one gradient handle, read with another copy of it
r1_0 = gen_data_flow_ops._tensor_array_read(g_h_1, 0, tf.float32)
t_g_h_0, t_g_h_1, d_r1_0 = sess.run([g_h_0, g_h_1, r1_0])
self.assertAllEqual(t_g_h_0, t_g_h_1)
self.assertAllEqual([[4.0, 5.0]], d_r1_0)
def _testTensorArrayWriteGradientAddMultipleAddsType(self, use_gpu, dtype):
with self.test_session(use_gpu=use_gpu):
h = gen_data_flow_ops._tensor_array(
dtype=dtype, tensor_array_name="foo", size=3)
c = lambda x: np.asarray(x, dtype=dtype.as_numpy_dtype)
writes = [
gen_data_flow_ops._tensor_array_write(
h, 2, c(3.0), gradient_add=True),
gen_data_flow_ops._tensor_array_write(
h, 2, c(4.0), gradient_add=True)]
with tf.control_dependencies(writes):
self.assertAllEqual(
c(7.00), gen_data_flow_ops._tensor_array_read(h, 2, dtype).eval())
def read(self, index, name=None):
"""Read the value at location `index` in the TensorArray.
Args:
index: 0-D. int32 tensor with the index to read from.
name: A name for the operation (optional).
Returns:
The tensor at index `index`.
"""
with ops.colocate_with(self._handle):
value = gen_data_flow_ops._tensor_array_read(handle=self._handle,
index=index,
flow_in=self._flow,
dtype=self._dtype,
name=name)
return value
def _testTensorArrayUnpackRead(self, tf_dtype, use_gpu):
dtype = tf_dtype.as_numpy_dtype()
with self.test_session(use_gpu=use_gpu) as sess:
h = gen_data_flow_ops._tensor_array(
dtype=tf_dtype, tensor_array_name="foo", size=3)
if tf_dtype == tf.string:
convert = lambda x: np.asarray(x).astype(np.str)
else:
convert = lambda x: np.asarray(x).astype(dtype)
# Unpack a vector into scalars
with tf.control_dependencies([
gen_data_flow_ops._tensor_array_unpack(
h, convert([1.0, 2.0, 3.0]))]):
r0 = gen_data_flow_ops._tensor_array_read(h, 0, tf_dtype)
r1 = gen_data_flow_ops._tensor_array_read(h, 1, tf_dtype)
r2 = gen_data_flow_ops._tensor_array_read(h, 2, tf_dtype)
d0, d1, d2 = sess.run([r0, r1, r2])
self.assertAllEqual(convert(1.0), d0)
self.assertAllEqual(convert(2.0), d1)
self.assertAllEqual(convert(3.0), d2)
# Unpack a matrix into vectors
with tf.control_dependencies([
gen_data_flow_ops._tensor_array_unpack(
h, convert([[1.0, 1.1], [2.0, 2.1], [3.0, 3.1]]))]):
r0 = gen_data_flow_ops._tensor_array_read(h, 0, tf_dtype)
r1 = gen_data_flow_ops._tensor_array_read(h, 1, tf_dtype)
r2 = gen_data_flow_ops._tensor_array_read(h, 2, tf_dtype)
d0, d1, d2 = sess.run([r0, r1, r2])
self.assertAllEqual(convert([1.0, 1.1]), d0)
self.assertAllEqual(convert([2.0, 2.1]), d1)
self.assertAllEqual(convert([3.0, 3.1]), d2)
def read(self, index, name=None):
"""Read the value at location `index` in the TensorArray."""
value = gen_data_flow_ops._tensor_array_read(
handle=self._handle, index=index, flow_in=self._flow, dtype=self._dtype,
name=name)
return value
