Error in the 'Conv2D' op in IndianPinesCNN class

[sayantanghosh]

Hello, I just noticed a strange error while running the Decoder Spatial CNN notebook before getting into the code. I am running it with python3.5 and tensorflow 1.4. Just thought of bringing it to your notice. Specifically, the IndianPinesCNN.inference(), execution of line 70:

z = tf.nn.conv2d(x_image, weights, strides=[1, 1, 1, 1], padding='VALID')

raised the error:

InvalidArgumentError: Negative dimension size caused by subtracting 3 from 1 for 'conv_1/Conv2D' (op: 'Conv2D') with input shapes: [1,1,1,220], [3,3,220,500]

Here is the output when I run the decoder():

---

InvalidArgumentError Traceback (most recent call last)
/usr/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)
653 graph_def_version, node_def_str, input_shapes, input_tensors,
--> 654 input_tensors_as_shapes, status)
655 except errors.InvalidArgumentError as err:

/usr/lib64/python3.5/contextlib.py in exit(self, type, value, traceback)
65 try:
---> 66 next(self.gen)
67 except StopIteration:

/usr/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py in raise_exception_on_not_ok_status()
465 compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466 pywrap_tensorflow.TF_GetCode(status))
467 finally:

InvalidArgumentError: Negative dimension size caused by subtracting 3 from 1 for 'conv_1/Conv2D' (op: 'Conv2D') with input shapes: [1,1,1,220], [3,3,220,500].

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last)
in ()
----> 1 predicted_image,predicted_results = decoder()

in decoder()
11 conv2,
12 fc1,
---> 13 fc2)
14
15 eval_correct = IndianPinesCNN.evaluation(logits, labels_placeholder)

/home/Hyperspectral-master/IndianPinesCNN.py in inference(images, conv1_channels, conv2_channels, fc1_units, fc2_units)
68 # Flattening the 3D image into a 1D array
69 x_image = tf.reshape(images, [-1,IMAGE_SIZE,IMAGE_SIZE,CHANNELS])
---> 70 z = tf.nn.conv2d(x_image, weights, strides=[1, 1, 1, 1], padding='VALID')
71 print (z)
72 h_conv1 = tf.nn.relu(z+biases, name=scope.name)

/usr/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py in conv2d(input, filter, strides, padding, use_cudnn_on_gpu, data_format, name)
395 strides=strides, padding=padding,
396 use_cudnn_on_gpu=use_cudnn_on_gpu,
--> 397 data_format=data_format, name=name)
398 return result
399

/usr/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py in apply_op(self, op_type_name, name, **keywords)
765 op = g.create_op(op_type_name, inputs, output_types, name=scope,
766 input_types=input_types, attrs=attr_protos,
--> 767 op_def=op_def)
768 if output_structure:
769 outputs = op.outputs

/usr/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in create_op(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)
2630 original_op=self._default_original_op, op_def=op_def)
2631 if compute_shapes:
-> 2632 set_shapes_for_outputs(ret)
2633 self._add_op(ret)
2634 self._record_op_seen_by_control_dependencies(ret)

/usr/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in set_shapes_for_outputs(op)
1909 shape_func = _call_cpp_shape_fn_and_require_op
1910
-> 1911 shapes = shape_func(op)
1912 if shapes is None:
1913 raise RuntimeError(

/usr/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in call_with_requiring(op)
1859
1860 def call_with_requiring(op):
-> 1861 return call_cpp_shape_fn(op, require_shape_fn=True)
1862
1863 _call_cpp_shape_fn_and_require_op = call_with_requiring

/usr/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py in call_cpp_shape_fn(op, require_shape_fn)
593 res = _call_cpp_shape_fn_impl(op, input_tensors_needed,
594 input_tensors_as_shapes_needed,
--> 595 require_shape_fn)
596 if not isinstance(res, dict):
597 # Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op).

/usr/lib/python3.5/site-packages/tensorflow/python/framework/common_shapes.py in _call_cpp_shape_fn_impl(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)
657 missing_shape_fn = True
658 else:
--> 659 raise ValueError(err.message)
660
661 if missing_shape_fn:

ValueError: Negative dimension size caused by subtracting 3 from 1 for 'conv_1/Conv2D' (op: 'Conv2D') with input shapes: [1,1,1,220], [3,3,220,500].

I have the same question @sayantanghosh , how did you solve it?

[sayantanghosh]

@Hatinennshiro: I rewrote the code with the same parameters and architecture, but also with tensorboard support. The tensorboard visualisation is a little rough around the edges though.

[HKCaesar]

@Hatinennshiro did you solve it?

[ariasfx]

Did anyone get to a solution for this? I'm getting the same error, albeit with different dimensions:

InvalidArgumentError: Negative dimension size caused by subtracting 3 from 1 for 'conv_1/Conv2D' (op: 'Conv2D') with input shapes: [100,1,1,220], [3,3,220,500].

[sidbala2310]

@sayantanghosh do you mind sharing the code for how you solved the issue? I'm still running into this error

[sayantanghosh]

@sidbala2310 I suggest that you follow the tf API documentation and rewrite the code with the more recent tf.estimator module.

[HAOJIEFENGLANG]

@ariasfx did you solve it ? do you mind sharing the code for how you solved the issue?

[HAOJIEFENGLANG]

@HKCaesar did you solve it ? do you mind sharing the code for how you solved the issue?

[sayantanghosh]

@HAOJIEFENGLANG @sidbala2310 @ariasfx I will fork and upload the code in a couple of days.

[sandeep-25]

Can anyone send the modified code to sandeepkmriacr@gmail.com for the error-
Negative dimension size caused by subtracting 3 from 1 for 'conv_1/Conv2D' (op: 'Conv2D') with input shapes: [100,1,1,220], [3,3,220,500].

[sohailkhanmarwat]

Replace this line
z = tf.nn.conv2d(x_image, weights, strides=[1, 1, 1, 1], padding='VALID')
to
z = tf.nn.conv2d(x_image, weights, strides=[1, 1, 1, 1], padding='SAME')
in inference fucntion of IndianPinesCNN.py file

[Rambo-7]

Replace this line
z = tf.nn.conv2d(x_image, weights, strides=[1, 1, 1, 1], padding='VALID')
to
z = tf.nn.conv2d(x_image, weights, strides=[1, 1, 1, 1], padding='SAME')
in inference fucntion of IndianPinesCNN.py file

can't fix it...