I'm working on a neural net problem, and in the conv2d function, i'm getting a Type mismatch issue.
Here's the code snippet
conv_layer1 = tf.nn.conv2d(inputs, w_layer1, strides=strides, padding='VALID') + b_layer1
I'm getting this error
TypeError: Expected binary or unicode string, got <bound method Kernel.raw_input of <ipykernel.ipkernel.IPythonKernel object at 0x000001C0A75CB470>>
I tried having [1,1,1,1] inline aswell as a variable, but no luck
The complete error trace is here(search for "In [46]:"
https://github.com/mymachinelearnings/CarND-Traffic-Sign-Classifier-Project/blob/attempt1/Traffic_Sign_Classifier.ipynb
Looks like a typo. In your notebook, you're feeding input into your network which is a built-in python method to get input from e.g. a keyboard. Obviously this doesn't make much sense as input to a convolutional network. Chances are you meant to type inputs as in your question?
Note that the syntax highlighting in the notebook shows this quite clearly -- input is displayed in green (at least in my browser) signifying that it has a special meaning.
Related
I am currently learning tensorflow, and I have run into an issue with
tf.nn.sigmoid_cross_entropy_with_logits(labels=y,logits=logits). The function description says that both labels and logits must be of the same type. I have the function below that I am using to classify MNIST images. The following are key section of my code
X=tf.placeholder(tf.float32,shape=(None,n_inputs),name="X")
y=tf.placeholder(tf.int32,shape=(None),name="y")
def neuron_layer(X,W,b,n_neurons,name,activation=None):
with tf.name_scope(name):
n_inputs=int(X.get_shape()[1])
stddev=2/np.sqrt(n_inputs)
z=tf.matmul(X,W)+b
if activation=="sigmoid":
return tf.math.sigmoid(z)
else:
return z
with tf.name_scope("dnn"):
hidden1=neuron_layer(X,W1,b1,n_hidden1,"hidden",activation="sigmoid")
logits=neuron_layer(hidden1,W2,b2,n_outputs,"outputs",activation="sigmoid")
with tf.name_scope("loss"):
xentropy=tf.nn.sigmoid_cross_entropy_with_logits(labels=y,logits=logits)
loss=tf.reduce_mean(xentropy,name="loss")
I get the error: input 'y' of 'Mul' Op has type int32 that does not match type float32 of argument 'x
if I change
y=tf.placeholder(tf.float32,shape=(None),name="y"). I get the error
Value passed to parameter 'targets' has DataType float32 not in list of allowed values: int32, int64. Yet logits can only be float32 or float64. Please help me fix the issue. Thanks
As mentioned in the comments, tf.nn.sigmoid_cross_entropy_with_logits is the wrong function. In your case you should use tf.nn.softmax_cross_entropy_with_logits instead (actually, that one yields a deprecation warning, so tf.nn.softmax_cross_entropy_with_logits_v2 is the correct one). Also note, as also mentioned in the comments, that the point of these two functions is that they have a sigmoid (or softmax, respectively) built in, so your model shouldn't have any activation function on the last layer.
Regarding the issue: I just tried it with tensorflow version 1.14.0. There, the issue still occurs if y has type int32. However, it works smoothly if both, y and labels, have type float32.
It's kind of inconsistent that tf.nn.sigmoid_cross_entropy_with_logits does not perform this cast itself, while tf.nn.softmax_cross_entropy_with_logits has no issue with y being int32.
I am new to TensorFlow and just went through the eager execution tutorial and came across the tf.decode_csv function. Not knowing about it, I read the documentation. https://www.tensorflow.org/api_docs/python/tf/decode_csv
I don't really understand it.
The documentation says 'records: A Tensor of type string.'
So, my question is: What qualifies as a 'Tensor'?
I tried the following code:
dec_res = tf.decode_csv('0.1,0.2,0.3', [[0.0], [0.0], [0.0]])
print(dec_res, type(dec_res))
l = [[1,2,3],[4,5,6],[7,8,9]]
r = tf.reshape(l, [9,-1])
print(l, type(l))
print(r, type(r))
So the list dec_res contains tf.tensor objects. That seems reasonable to me. But is an ordinary string also a 'Tensor' according to the documentation?
Then I tried something else with the tf.reshape function. In the documentation https://www.tensorflow.org/api_docs/python/tf/reshape it says that 'tensor: A Tensor.' So, l is supposed to be a tensor. But it is not of type tf.tensor but simply a python list. This is confusing.
Then the documentation says
Returns:
A Tensor. Has the same type as tensor.
But the type of l is list where the type of r is tensorflow.python.framework.ops.Tensor. So the types are not the same.
Then I thought that TensorFlow is very generous with things being a tensor. So I tried:
class car(object):
def __init__(self, color):
self.color = color
red_car = car('red')
#test_reshape = tf.reshape(red_car, [1, -1])
print(red_car.color) # to check, that red_car exists.
Now, the line in comments results in an error.
So, can anyone help me to find out, what qualifies as a 'Tensor'?
P.S.: I tried to read the source code of tf.reshape as given in the documentation
Defined in tensorflow/python/ops/gen_array_ops.py.
But this file does not exist in the Github repo. Does anyone know how to read it?
https://www.tensorflow.org/programmers_guide/tensors
TensorFlow, as the name indicates, is a framework to define and run
computations involving tensors. A tensor is a generalization of
vectors and matrices to potentially higher dimensions. Internally,
TensorFlow represents tensors as n-dimensional arrays of base
datatypes.
What you are observing commes from the fact that tensorflow operations (like reshape) can be built from various python types using the function tf.convert_to_tensor:
https://www.tensorflow.org/api_docs/python/tf/convert_to_tensor
All standard Python op constructors apply this function to each of
their Tensor-valued inputs, which allows those ops to accept numpy
arrays, Python lists, and scalars in addition to Tensor objects
Unlike most programming languages, TensorFlow does not regard the shape of an array as part of the type. The downside of this is that, if you make a mistake and accidentally provide data of the wrong shape, it may silently give a wrong answer e.g. Slightly different shape converges to wrong number - why? which makes debugging difficult.
Does there exist a shape checker for TF? That is, a function or program that can analyze a graph (with sample feed_dict if need be) and raise the alarm if there is a shape mismatch?
Tensorflow does offer a shape checker mechanism which is technically the shape argument you should specify while declaring Tensorflow place holders. By default, tensorflow takes [None,None] for shape. But , for example if you do specify the shape while declaring your place holders, then it will raise shape error whenever user enters data of incorrect/conflicting shape. For example
lets say I declared a place holder named X and did specify its shape argument too:
X=tf.placeholder(dtype=tf.float32, shape=[None,256])
Now, this means that number of rows of X can vary but number of features will always be 256. And now , if I mistakenly feed data of shape lets say 1000 rows and 20 features, shape error will be raised.
Also, check this link :https://www.tensorflow.org/api_docs/python/tf/placeholder
Use:
print(str(tf.Shape(test_tensor))) # where test_tensor is
whatever your tensor's name is
Documentation available here: https://www.tensorflow.org/api_docs/python/tf/shape
I'm getting a TypeError("List of Tensors when single Tensor expected") when I run a Tensorflow while_loop. The error is from the third parameter, which should be a list of Tensors, according to the documentation. x, W, Win, Y, temp, and Wout are all previously declared as floats and arrays of floats. cond2 and test2 are functions I've written to be the condition and body. I use an almost identical call earlier in the program with no issues.
t=0
t,x,W,Win,Y,temp,Wout = sess.run(tf.while_loop(cond2, test2,
[t, tf.Variable(x), tf.constant(W),
tf.constant(Win), tf.Variable(Y),
tf.Variable(temp), tf.constant(Wout)],
shape_invariants=[tf.TensorShape(None),
tf.TensorShape(None),
tf.TensorShape(None),
tf.TensorShape(None),
tf.TensorShape(None),
tf.TensorShape(None),
tf.TensorShape(None)]))
I fixed the error by removing the tf.constant() for Wout, since Wout was already declared as a tensor.
This would be easier to diagnose with (a) your definitions for condition and body, and (b) the full error output from TensorFlow (it usually also outputs a full dump of the input tensors when issuing these errors.)
With that said, the source of the problem seems to be that TensorFlow is viewing your loop_vars list as a single Tensor, and/or your cond2 and test2 functions only accept a single argument each. If neither of these is true, then providing more detail would help answer the question (specifically the full error message and the definition for every value/tensor/function you're passing to tf.while_loop. I've found that the majority of while_loop errors can be fixed by paying attention to the tensors in the error output.
The while_loop can throw pretty confusing errors at times so I'd like to help; I'll check back and update/edit my answer if more info is provided.
I have an image that is 478 x 717 x 3 = 1028178 pixels, with a rank of 1. I verified it by calling tf.shape and tf.rank.
When I call image.set_shape([478, 717, 3]), it throws the following error.
"Shapes %s and %s must have the same rank" % (self, other))
ValueError: Shapes (?,) and (478, 717, 3) must have the same rank
I tested again by first casting to 1028178, but the error still exists.
ValueError: Shapes (1028178,) and (478, 717, 3) must have the same rank
Well, that does make sense because one is of rank 1 and the other is of rank 3. However, why is it necessary to throw an error, as the total number of pixels still match.
I could of course use tf.reshape and it works, but I think that's not optimal.
As stated on the TensorFlow FAQ
What is the difference between x.set_shape() and x = tf.reshape(x)?
The tf.Tensor.set_shape() method updates the static shape of a Tensor
object, and it is typically used to provide additional shape
information when this cannot be inferred directly. It does not change
the dynamic shape of the tensor.
The tf.reshape() operation creates a new tensor with a different dynamic shape.
Creating a new tensor involves memory allocation and that could potentially be more costly when more training examples are involved. Is this by design, or am I missing something here?
As far as I know (and I wrote that code), there isn't a bug in Tensor.set_shape(). I think the misunderstanding stems from the confusing name of that method.
To elaborate on the FAQ entry you quoted, Tensor.set_shape() is a pure-Python function that improves the shape information for a given tf.Tensor object. By "improves", I mean "makes more specific".
Therefore, when you have a Tensor object t with shape (?,), that is a one-dimensional tensor of unknown length. You can call t.set_shape((1028178,)), and then t will have shape (1028178,) when you call t.get_shape(). This doesn't affect the underlying storage, or indeed anything on the backend: it merely means that subsequent shape inference using t can rely on the assertion that it is a vector of length 1028178.
If t has shape (?,), a call to t.set_shape((478, 717, 3)) will fail, because TensorFlow already knows that t is a vector, so it cannot have shape (478, 717, 3). If you want to make a new Tensor with that shape from the contents of t, you can use reshaped_t = tf.reshape(t, (478, 717, 3)). This creates a new tf.Tensor object in Python; the actual implementation of tf.reshape() does this using a shallow copy of the tensor buffer, so it is inexpensive in practice.
One analogy is that Tensor.set_shape() is like a run-time cast in an object-oriented language like Java. For example, if you have a pointer to an Object but know that, in fact, it is a String, you might do the cast (String) obj in order to pass obj to a method that expects a String argument. However, if you have a String s and try to cast it to a java.util.Vector, the compiler will give you an error, because these two types are unrelated.