Tensorflow cannot calculate gradient of operation - c++

Question

I have a simple Tensorflow model which is written in python and could be trained smoothly. Here is the model I'm using:
import tensorflow as tf from tensorflow.python.framework
import ops from tensorflow.python.framework
import dtypes
import random import numpy as np
NUM_CLASSES = 102 IMAGE_HEIGHT = 224 IMAGE_WIDTH = 224 BATCH_SIZE = 25 NUM_CHANNELS = 3 LEARNING_RATE = 0.0001
with tf.Session() as sess:
images_placeholder = tf.placeholder (tf.float32,
shape=(BATCH_SIZE, IMAGE_HEIGHT,
IMAGE_WIDTH, NUM_CHANNELS), name="input") labels_placeholder = tf.placeholder (tf.float32,
shape=(BATCH_SIZE), name="label")
with tf.name_scope("conv1_1") as scope: kernel = tf.Variable (tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32, stddev=1e-2),
name="weights") conv = tf.nn.conv2d (images_placeholder, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable (tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add (conv, biases) conv1_1 = tf.nn.relu (out, name=scope)
pool1 = tf.nn.max_pool (conv1_1,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool1')
with tf.name_scope('conv2_1') as scope: kernel = tf.Variable (tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32,
stddev=1e-2), name='weights') conv = tf.nn.conv2d (pool1, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable (tf.constant(0.0, shape=[128], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add (conv, biases) conv2_1 = tf.nn.relu (out, name=scope)
pool2 = tf.nn.max_pool (conv2_1,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool2')
with tf.name_scope('conv3_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32,
stddev=1e-2), name='weights') conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) conv3_1 = tf.nn.relu(out, name=scope)
pool3 = tf.nn.max_pool (conv3_1,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool3')
with tf.name_scope('conv4_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 512], dtype=tf.float32,
stddev=1e-2), name='weights') conv = tf.nn.conv2d(pool3, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) conv4_1 = tf.nn.relu(out, name=scope)
pool4 = tf.nn.max_pool (conv4_1,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool4')
with tf.name_scope('mentee_conv5_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-2), name='weights') conv = tf.nn.conv2d(pool4, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) conv5_1 = tf.nn.relu(out, name=scope)
pool5 = tf.nn.max_pool (conv5_1,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool5')
with tf.name_scope('fc1') as scope: shape = int(np.prod(pool5.get_shape()[1:])) fc1w = tf.Variable(tf.truncated_normal([shape, 4096], dtype=tf.float32,
stddev=1e-2), name='weights') fc1b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
trainable=True, name='biases') pool5_flat = tf.reshape(pool5, [-1, shape]) fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b) fc1 = tf.nn.relu(fc1l)
fc1 = tf.nn.dropout(fc1, 0.5)
labels = tf.to_int64(labels_placeholder) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits (labels=labels,
logits=fc1, name="xentropy") loss = tf.reduce_mean (cross_entropy, name='loss')
optimizer = tf.train.AdamOptimizer (LEARNING_RATE) global_step = tf.Variable (0, name='global_step', trainable=False) train_op = optimizer.minimize (loss, global_step=global_step, name="train")
init = tf.initialize_variables (tf.all_variables(), name='init_all_vars_op') tf.train.write_graph (sess.graph_def, "models/", "graph.pb", as_text=False)
I'm exporting the model into the protobuf version and then load it into my C++ code, in order to train the model using C++ API instead of Python. Unfortunately I'll get an error regarding the SparseSoftmaxCrossEntropy operation as:
E tensorflow/core/common_runtime/executor.cc:594] Executor failed to create kernel. Invalid argument: NodeDef mentions attr 'message' not in Op<name=PreventGradient; signature=input:T -> output:T; attr=T:type>; NodeDef: gradients/xentropy/xentropy_grad/PreventGradient = PreventGradient[T=DT_FLOAT, message="Currently there is no way to take the second derivative of sparse_softmax_cross_entropy_with_logits due to the fused implementation\'s interaction with tf.gradients()", _device="/job:localhost/replica:0/task:0/cpu:0"](xentropy/xentropy:1)
I have checked tensorflow source code and realized the gradient caclulation for this operation has not been implemented. But I'm wondering how python version succeeds with no issue, but in C++ it tries to calculate the gradient?