CODEWITHSUNDEEP
pythontensorflowtensorflow-serving
Brian Phiri
Brian Phiri

Reputation: 154

Tensor model input - nvalidArgumentError (see above for traceback): Shape in shape_and_slice spec

Hey I am trying to set an input point for the model that i have writen in tensorflow this is the code for the classification

n_dim = training_features.shape[1]
x = tf.placeholder(tf.float32, [None,n_dim])

classifier = (...)
init_op = tf.initialize_all_variables()
with tf.Session() as sess:
    sess.run(init_op)
    classifier.fit(training_features, training_labels, steps=100)
    accuracy_score = classifier.evaluate(testing_features, testing_labels, steps=100)["accuracy"]
    print('Accuracy', accuracy_score)

    pred_a = np.asarray([x])
    prediction = format(list(classifier.predict(pred_a)))
    prediction_result = np.array(prediction)
    output = tf.convert_to_tensor(prediction_result,dtype=None,name="output", preferred_dtype=None)

and here is my building code 

export_path_base = sys.argv[-1]
export_path = os.path.join(
    compat.as_bytes(export_path_base),
    compat.as_bytes(str(FLAGS.model_version)))
print('Exporting trained model to', export_path)
builder = saved_model_builder.SavedModelBuilder(export_path)

classification_inputs = utils.build_tensor_info(y)
classification_outputs_classes = utils.build_tensor_info(output)

print('classification_signature...')
classification_signature = signature_def_utils.build_signature_def(
    inputs={signature_constants.CLASSIFY_INPUTS: classification_inputs},
    outputs={
        signature_constants.CLASSIFY_OUTPUT_CLASSES:
            classification_outputs_classes
    },
    method_name=signature_constants.CLASSIFY_METHOD_NAME)
tensor_info_x = utils.build_tensor_info(x)

print('prediction_signature...')
prediction_signature = signature_def_utils.build_signature_def(
    inputs={'input': tensor_info_x},
    outputs={
        'classes' : classification_outputs_classes
    },
    method_name=signature_constants.PREDICT_METHOD_NAME)
print('Exporting...')
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
    sess, [tag_constants.SERVING],
    signature_def_map={
        'predict_sound':
            prediction_signature,
        signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
            classification_signature,
    },
    legacy_init_op=legacy_init_op)
builder.save()
print('Saved...')

I have tried manually passing dummy data before the build and that works but i am trying to have the client stub pass data into the model dynamically. When i try run that code to build i get this error

InvalidArgumentError (see above for traceback): Shape in shape_and_slice spec [1,280] does not match the shape stored in checkpoint: [193,280] [[Node: save/RestoreV2_1 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2_1/tensor_names, save/RestoreV2_1/shape_and_slices)]]

May main goal is to have x as an input and output return the results, the output works but cant get the input to work.

Upvotes: 0

Views: 989

Answers (1)

Qin Heyang
Qin Heyang

Reputation: 1674

Edit: If you just put the np.array as input without going through an input function, it will work but you also give up the chance to check the input.

Tensorflow won't check your input even if it's in the wrong shape or type or somehow corrupted but to throw such an error in the middle of session. Since you can run it with your test data successfully, the problem should be your actual data. Thus, it's recommended to write an input function to check your data before put it into classifier. Note that input function should return tf.Tensor with the shape of [x,1] (x is the number of your features) instead of an np.array.

Please refer to https://www.tensorflow.org/get_started/input_fn to see how to write your own input function and pass it to the classifier.

An example of input function:

def input_fn_predict(): # returns x, None
    #do your check here or you can just print it out
    feature_tensor = tf.constant(pred_a,shape=[1,pred_a.size])
    return feature_tensor,None

Upvotes: 1

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