Question

我希望在Android上使用Tensorflow，使用在GPU上训练的pb文件。

Android上Tensorflow-demo的官方示例使用预先训练的'tensorflow_inception_graph.pb'。如上所述，我想在Android上使用我的'* .pb'。

首先，我训练了简单的Tensorflow代码，类似于下面的python上的mnist-example。

python代码

def deepnn(x):
    with tf.name_scope('reshape'):
    x_image = tf.reshape(x, [-1, 30, 6, 1])

    with tf.name_scope('conv1'):
        W_conv1 = weight_variable([5, 5, 1, 32])
        b_conv1 = bias_variable([32])
        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

    with tf.name_scope('conv2'):
        W_conv2 = weight_variable([5, 5, 32, 64])
        b_conv2 = bias_variable([64])
        h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2)

    with tf.name_scope('pool2'):
        h_pool2 = max_pool_2x2(h_conv2)

    with tf.name_scope('fc1'):
        W_fc1 = weight_variable([15 * 3 * 64, 1024])
        b_fc1 = bias_variable([1024])

        h_pool2_flat = tf.reshape(h_pool2, [-1, 15 * 3 * 64])
        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    with tf.name_scope('dropout'):
        keep_prob = tf.placeholder(tf.float32)
        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    with tf.name_scope('fc2'):
        W_fc2 = weight_variable([1024, 28])
        b_fc2 = bias_variable([28])

        y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

    return y_conv, keep_prob


def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)



x = tf.placeholder(tf.float32, [None, 180], name='input')
y_ = tf.placeholder(tf.int64, [None])

y_conv, keep_prob = deepnn(x)
y_conv = tf.identity(y_conv, 'output')

with tf.name_scope('loss'):
    cross_entropy = tf.losses.sparse_softmax_cross_entropy( labels=y_, logits=y_conv )
cross_entropy = tf.reduce_mean(cross_entropy, name='loss')

y_argout = tf.argmax(input=y_conv, axis=1, name="y_argout")

with tf.name_scope('adam_optimizer'):
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

with tf.name_scope('accuracy'):
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_)
    correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)

graph_location = tempfile.mkdtemp()
print('Saving graph to: %s' % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())

init_op = tf.global_variables_initializer()
saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(init_op)

    ...
    ...

    tf.train.write_graph(sess.graph_def, './', 'mlp.pb', as_text=False)

x = tf.placeholder(tf.float32, [None, 180], name='input') 和 y_conv = tf.identity(y_conv, 'output') 命名是在Android Studio上使用。

然后，我在Android Studio上使用输出'mlp.pb'，如下所示。

android studio上的java代码

inferenceInterface = new TensorFlowInferenceInterface(getAssets(), 'mlp.pb');

...

inferenceInterface.feed('input', floatValues, 180);
inferenceInterface.run(new String[] {'output'});　// an error will occur here.
inferenceInterface.fetch('output', outputs);

发生错误

06-08 09:33:59.500 22971-22971/com.example.tkt.tf_test E/TensorFlowInferenceInterface: Failed to run TensorFlow inference with inputs:[input], outputs:[output]
06-08 09:33:59.501 22971-22971/com.example.tkt.tf_test D/AndroidRuntime: Shutting down VM
06-08 09:33:59.584 22971-22971/com.example.tkt.tf_test E/AndroidRuntime: FATAL EXCEPTION: main
Process: com.example.tkt.tf_test, PID: 22971
java.lang.IllegalArgumentException: No OpKernel was registered to support Op 'SparseSoftmaxCrossEntropyWithLogits' with these attrs.  Registered devices: [CPU], Registered kernels:
  <no registered kernels>

     [[Node: loss/sparse_softmax_cross_entropy_loss/xentropy/xentropy = SparseSoftmaxCrossEntropyWithLogits[T=DT_FLOAT, Tlabels=DT_INT64](output, Placeholder)]]

问题

我只想获得名为'output'的变量，但为什么涉及'SparseSoftmaxCrossEntropyWithLogits'？
注册内核意味着什么。
我该怎么办？

我非常感谢您的回复。

Answer 1

问题 1：
tf.losses.sparse_softmax_cross_entropy

问题 2：
注册内核是指设备可以支持什么op内核。

问题 3：
在 android 中，只有一个内核子集来保持基本库很小。如果您想支持更多自定义 TensorFlow 运算符，您应该按照此 link
为了在android中支持SparseSoftmaxCrossEntropyWithLogits，您可以在BUILD文件中添加sparse_xent_op.cc和sparse_xent_op.h，如源文件中的xent_op.cc和xent_op.h。

没有Opkernel注册支持Op'SparseSoftmaxCrossEntropyWithLogits'

1 个答案: