def _testDuplicateTensorArrayFails(self, use_gpu): with self.test_session(use_gpu=use_gpu) as sess: h1 = gen_data_flow_ops._tensor_array( size=1, dtype=tf.float32, tensor_array_name="foo") c1 = gen_data_flow_ops._tensor_array_write(h1, 0, 4.0) h2 = gen_data_flow_ops._tensor_array( size=1, dtype=tf.float32, tensor_array_name="foo") c2 = gen_data_flow_ops._tensor_array_write(h2, 0, 5.0) with self.assertRaises(errors.AlreadyExistsError): sess.run([c1, c2])
def _testTensorArrayWriteMultipleFails(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 self.assertRaisesOpError( "Could not write to TensorArray index 2 because " "it has already been written to."): with tf.control_dependencies([ gen_data_flow_ops._tensor_array_write(h, 2, 3.0)]): gen_data_flow_ops._tensor_array_write(h, 2, 3.0).run()
def _testWriteCloseTensorArray(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, [3.0]), gen_data_flow_ops._tensor_array_write(h, 2, -1.0)] with tf.control_dependencies(writes): close = gen_data_flow_ops._tensor_array_close(h) sess.run(close)
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 _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 _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 write(self, index, value, name=None): """Write `value` into index `index` of the TensorArray. Args: index: 0-D. int32 scalar with the index to write to. value: N-D. Tensor of type `dtype`. The Tensor to write to this index. name: A name for the operation (optional). Returns: A new TensorArray object with flow that ensures the write occurs. Use this object all for subsequent operations. Raises: ValueError: if there are more writers than specified. """ with ops.colocate_with(self._handle): flow_out = gen_data_flow_ops._tensor_array_write( handle=self._handle, index=index, value=value, flow_in=self._flow, name=name ) ta = TensorArray(dtype=self._dtype, handle=self._handle) ta._flow = flow_out ta._infer_shape = self._infer_shape ta._elem_shape = self._elem_shape if ta._infer_shape: val_shape = flow_out.op.inputs[2].get_shape() if ta._elem_shape: if not val_shape == ta._elem_shape[0]: raise ValueError( "Inconsistent shapes: saw %s but expected %s " "(and infer_shape=True)" % (val_shape, ta._elem_shape[0]) ) else: ta._elem_shape.append(val_shape) return ta
def write(self, index, value, name=None): """Write `value` into index `index` of the TensorArray. Args: index: 0-D. int32 scalar with the index to write to. value: N-D. Tensor of type `dtype`. The Tensor to write to this index. name: A name for the operation (optional). Returns: A new TensorArray object with flow that ensures the write occurs. Use this object all for subsequent operations. Raises: ValueError: if there are more writers than specified. """ with ops.colocate_with(self._handle): flow_out = gen_data_flow_ops._tensor_array_write( handle=self._handle, index=index, value=value, flow_in=self._flow, name=name) ta = TensorArray(dtype=self._dtype, handle=self._handle) ta._flow = flow_out ta._infer_shape = self._infer_shape ta._elem_shape = self._elem_shape if ta._infer_shape: val_shape = flow_out.op.inputs[2].get_shape() if ta._elem_shape: if not val_shape == ta._elem_shape[0]: raise ValueError("Shape inference failed.") else: ta._elem_shape.append(val_shape) return ta
def write(self, index, value, name=None): """Write `value` into index `index` of the TensorArray.""" flow_out = gen_data_flow_ops._tensor_array_write( handle=self._handle, index=index, value=value, flow_in=self._flow, name=name) # Size below is ignored ta = TensorArray(dtype=self._dtype, size=-1, handle=self._handle) ta._flow = flow_out return ta
def write(self, index, value, name=None): """Write `value` into index `index` of the TensorArray.""" flow_out = gen_data_flow_ops._tensor_array_write( handle=self._handle, index=index, value=value, flow_in=self._flow, name=name) # Size below is ignored ta = TensorArray(dtype=self._dtype, handle=self._handle) ta._flow = flow_out return ta
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 testTensorArrayPackNotAllValuesAvailableFails(self): with self.test_session(): h = gen_data_flow_ops._tensor_array( dtype=tf.float32, tensor_array_name="foo", size=3) with self.assertRaisesOpError( "Could not read from TensorArray index 1 " "because it has not yet been written to."): with tf.control_dependencies([ gen_data_flow_ops._tensor_array_write(h, 0, [[4.0, 5.0]])]): gen_data_flow_ops._tensor_array_pack(h, tf.float32).eval()
def _testTensorArrayWritePack(self, tf_dtype, use_gpu): dtype = tf_dtype.as_numpy_dtype() with self.test_session(use_gpu=use_gpu): 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) writes = [ gen_data_flow_ops._tensor_array_write(h, 0, convert([[4.0, 5.0]])), gen_data_flow_ops._tensor_array_write(h, 1, convert([[6.0, 7.0]])), gen_data_flow_ops._tensor_array_write(h, 2, convert([[8.0, 9.0]]))] with tf.control_dependencies(writes): c0 = gen_data_flow_ops._tensor_array_pack(h, tf_dtype) self.assertAllEqual( convert([[[4.0, 5.0]], [[6.0, 7.0]], [[8.0, 9.0]]]), c0.eval())
def write(self, index, value): """Write `value` into index `index` of the TensorArray.""" flow_out = gen_data_flow_ops._tensor_array_write( handle=self._handle, index=index, value=value, flow_in=self._flow, gradient_add=self._gradient_add) # Size below is ignored ta = TensorArray(dtype=self._dtype, size=-1, handle=self._handle) ta._gradient_add = self._gradient_add ta._flow = flow_out return ta
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 write(self, index, value, name=None): """Write `value` into index `index` of the TensorArray. Args: index: 0-D. int32 scalar with the index to write to. value: N-D. Tensor of type `dtype`. The Tensor to write to this index. name: A name for the operation (optional). Returns: A new TensorArray object with flow that ensures the write occurs. Use this object all for subsequent operations. """ with ops.colocate_with(self._handle): flow_out = gen_data_flow_ops._tensor_array_write( handle=self._handle, index=index, value=value, flow_in=self._flow, name=name ) ta = TensorArray(dtype=self._dtype, handle=self._handle) ta._flow = flow_out return ta
def write(self, index, value, name=None): """Write `value` into index `index` of the TensorArray. Args: index: 0-D. int32 scalar with the index to write to. value: N-D. Tensor of type `dtype`. The Tensor to write to this index. name: A name for the operation (optional). Returns: A new TensorArray object with flow that ensures the write occurs. Use this object all for subsequent operations. """ with ops.colocate_with(self._handle): flow_out = gen_data_flow_ops._tensor_array_write( handle=self._handle, index=index, value=value, flow_in=self._flow, name=name) ta = TensorArray(dtype=self._dtype, handle=self._handle) ta._flow = flow_out return ta
def _testTensorArrayWriteWrongIndexOrDataTypeFails(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) # Test writing the wrong datatype with self.assertRaisesOpError( "TensorArray dtype is float but Op is trying to write dtype string"): gen_data_flow_ops._tensor_array_write(h, -1, "wrong_type_scalar").run() # Test writing to a negative index with self.assertRaisesOpError( "Tried to write to index -1 but array size is: 3"): gen_data_flow_ops._tensor_array_write(h, -1, 3.0).run() # Test reading from too large an index with self.assertRaisesOpError( "Tried to write to index 3 but array size is: 3"): gen_data_flow_ops._tensor_array_write(h, 3, 3.0).run()