android - Invalid argument: NodeDef mentions attr 'Tshape' not in Op -

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i getting error invalid argument: nodedef mentions attr 'tshape' not in op<name=reshape; signature=tensor:t, shape:int32 -> output:t; attr=t:type>; nodedef: y_groundtruth = reshape[t=dt_float, tshape=dt_int32](lout_add, y_groundtruth/shape) when attempting load file on android using tensorflow::status s = session->create(graph_def);.

people similar problems have mentioned upgrading tensorflow 0.9.0rc0 fixed problems. however, using current tensorflow build in jni-build.

could problem variables declared when generating protobuf file?

snippet

def reg_perceptron(t, weights, biases):     t = tf.nn.relu(tf.add(tf.matmul(t, weights['h1']), biases['b1']), name = "layer_1")     t = tf.nn.sigmoid(tf.add(tf.matmul(t, weights['h2']), biases['b2']), name = "layer_2")     t = tf.add(tf.matmul(t, weights['hout'], name="lout_matmul"), biases['bout'], name="lout_add")      return tf.reshape(t, [-1], name="y_groundtruth")  g = tf.graph() g.as_default():    ...    rg_weights = {     'h1': vs.get_variable("weights0", [n_input, n_hidden_1], initializer=tf.contrib.layers.xavier_initializer()),     'h2': vs.get_variable("weights1", [n_hidden_1, n_hidden_2], initializer=tf.contrib.layers.xavier_initializer()),     'hout': vs.get_variable("weightsout", [n_hidden_2, 1], initializer=tf.contrib.layers.xavier_initializer())     }       rg_biases = {     'b1': vs.get_variable("bias0", [n_hidden_1], initializer=init_ops.constant_initializer(bias_start)),     'b2': vs.get_variable("bias1", [n_hidden_2], initializer=init_ops.constant_initializer(bias_start)),     'bout': vs.get_variable("biasout", [1], initializer=init_ops.constant_initializer(bias_start))     }      pred = reg_perceptron(_x, rg_weights, rg_biases)     ... ...  g_2 = tf.graph() g_2.as_default():     ...     rg_weights_2 = {     'h1': vs.get_variable("weights0", [n_input, n_hidden_1], initializer=tf.contrib.layers.xavier_initializer()),     'h2': vs.get_variable("weights1", [n_hidden_1, n_hidden_2], initializer=tf.contrib.layers.xavier_initializer()),     'hout': vs.get_variable("weightsout", [n_hidden_2, 1], initializer=tf.contrib.layers.xavier_initializer())     }      rg_biases_2 = {     'b1': vs.get_variable("bias0", [n_hidden_1], initializer=init_ops.constant_initializer(bias_start)),     'b2': vs.get_variable("bias1", [n_hidden_2], initializer=init_ops.constant_initializer(bias_start)),     'bout': vs.get_variable("biasout", [1], initializer=init_ops.constant_initializer(bias_start))     }      pred_2 = reg_perceptron(_x_2, rg_weights_2, rg_biases_2)     ...

to stop post being cluttered, have uploaded code generate .pb-file , model create model on pastebin.

pbfilegeneration

model


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