What is the difference between these two?
1- tf.reshape(tensor, [-1])
2- tf.reshape(tensor, -1)
I can not find any difference between these two, but when I use -1 without brackets, an error occurs when trying to map the function to a TensorSliceDataset.
Here is the simplified version of the code:
def reshapeME(tensor):
reshaped = tf.reshape(tensor,-1)
return reshaped
new_y_test = y_test.map(reshapeME)
and here is the Error:
ValueError: Shape must be rank 1 but is rank 0 for '{{node Reshape}} = Reshape[T=DT_FLOAT, Tshape=DT_INT32](one_hot, Reshape/shape)' with input shapes: [6], [].
If I add the bracket, there is no error. Also, there is no error when the function is used by calling and feeding a tensor.
tf.reshape expects a tensor or tensor-like variable as the shape in Graph mode:
A Tensor. Must be one of the following types: int32, int64. Defines the shape of the output tensor.
So, simple scalars will not work in this case. The map function of a tf.data.Dataset is always executed in Graph mode:
Note that irrespective of the context in which map_func is defined
(eager vs. graph), tf.data traces the function and executes it as a
graph.
Related
I have a n-D array. I need to create a 1-D range tensor based on dimensions.
for an example:
x = tf.placeholder(tf.float32, shape=[None,4])
r = tf.range(start=0, limit=, delta=x.shape[0],dtype=tf.int32, name='range')
sess = tf.Session()
result = sess.run(r, feed_dict={x: raw_lidar})
print(r)
The problem is, x.shape[0] is none at the time of building computational graph. So I can not build the tensor using range. It gives an error.
ValueError: Cannot convert an unknown Dimension to a Tensor: ?
Any suggestion or help for the problem.
Thanks in advance
x.shape[0] might not exist yet when running this code is graph mode. If you want a value, you need to use tf.shape(x)[0].
More information about that behaviour in the documentation for tf.Tensor.get_shape. An excerpt (emphasis is mine):
tf.Tensor.get_shape() is equivalent to tf.Tensor.shape.
When executing in a tf.function or building a model using tf.keras.Input, Tensor.shape may return a partial shape (including None for unknown dimensions). See tf.TensorShape for more details.
>>> inputs = tf.keras.Input(shape = [10])
>>> # Unknown batch size
>>> print(inputs.shape)
(None, 10)
The shape is computed using shape inference functions that are registered for each tf.Operation.
The returned tf.TensorShape is determined at build time, without executing the underlying kernel. It is not a tf.Tensor. If you need a shape tensor, either convert the tf.TensorShape to a tf.constant, or use the tf.shape(tensor) function, which returns the tensor's shape at execution time.
I have an existing complex model. Inside there is tensor x with shape (None, 128, 128, 3). First axis has dynamic shape, that should be materialized when batch is passed to feed_dict in session.run. However when I attempt to define broadcast operation to shape of x:
y = tf.broadcast_to(z, (x.shape[0], x.shape[1], x.shape[2], 1))
Exception is raised:
Failed to convert object of type <class 'tuple'> to
Tensor. Contents: (Dimension(None), Dimension(128),
Dimension(128), 1). Consider casting elements to a supported type.
Exception occurs when creating model, not when running it. Converting first element to number helps, but this is not the solution.
The .shape attribute gives you the shape known at graph construction time, which is a tf.TensorShape structure. If the shape of x were fully known, you could get your code to work as follows:
y = tf.broadcast_to(z, (x.shape[0].value, x.shape[1].value, x.shape[2].value, 1))
However, in your case x has an unknown first dimension. In order to use the actual tensor shape as a regular tf.Tensor (with value only known at runtime), you can use tf.shape:
x_shape = tf.shape(x)
y = tf.broadcast_to(z, (x_shape[0], x_shape[1], x_shape[2], 1))
I would like to slice a tensor and store it in a variable. Slicing with fixed numbers works fine eg: t[0:2]. But slicing a variable with another tensor doesnt work. eg t[t1:t2] Also storing the slice in a tensor works fine but when I try to store it in a tf.Variable i get errors.
import tensorflow as tf
import numpy
i=tf.zeros([2,1],tf.int32)
i2=tf.get_variable('i2_variable',initializer=i) #putting a multidimensional tensor in a variable
i4=tf.ones([10,1],tf.int32)
sess=tf.Session()
sess.run(tf.global_variables_initializer()) #initializing variables
itr=tf.constant(0,tf.int32)
def w_c(i2,itr):
return tf.less(itr,2)
def w_b(i2,itr):
i2=i4[(itr*0):((itr*0)+2)] #doesnt work
#i2=i4[0:2] #works
#i=i4[(itr*0):((itr*0)+2)] #works with tensor i
itr=tf.add(itr,1)
return[i2,itr]
OP=tf.while_loop(w_c,w_b,[i2,itr])
print(sess.run(OP))
I get following error:
ValueError: Input tensor 'i2_variable/read:0' enters the
loop with shape (2, 1), but has shape (?, 1) after one iteration.
To allow the shape to vary across iterations,
use the `shape_invariants` argument of tf.while_loop to specify a less-specific shape.
The code does not throw the error if you specify shape_invariants.
OP=tf.while_loop(w_c,w_b,[i2,itr],shape_invariants=
[ tf.TensorShape([None, None]),
itr.get_shape()])
It returns this.
[array([[1],
[1]]), 2]
In ipython I imported tensorflow as tf and numpy as np and created an TensorFlow InteractiveSession.
When I am running or initializing some normal distribution with numpy input, everything runs fine:
some_test = tf.constant(np.random.normal(loc=0.0, scale=1.0, size=(2, 2)))
session.run(some_test)
Returns:
array([[-0.04152317, 0.19786302],
[-0.68232622, -0.23439092]])
Just as expected.
...but when I use the Tensorflow normal distribution function:
some_test = tf.constant(tf.random_normal([2, 2], mean=0.0, stddev=1.0, dtype=tf.float32))
session.run(some_test)
...it raises a Type error saying:
(...)
TypeError: List of Tensors when single Tensor expected
What am I missing here?
The output of:
sess.run(tf.random_normal([2, 2], mean=0.0, stddev=1.0, dtype=tf.float32))
alone returns the exact same thing which np.random.normal generates -> a matrix of shape (2, 2) with values taken from a normal distribution.
The tf.constant() op takes a numpy array (or something implicitly convertible to a numpy array), and returns a tf.Tensor whose value is the same as that array. It does not accept a tf.Tensor as its argument.
On the other hand, the tf.random_normal() op returns a tf.Tensor whose value is generated randomly according to the given distribution each time it runs. Since it returns a tf.Tensor, it cannot be used as the argument to tf.constant(). This explains the TypeError (which is unrelated to the use of tf.InteractiveSession, since it occurs when you build the graph).
I'm assuming you want your graph to include a tensor that (i) is randomly generated on its first use, and (ii) constant thereafter. There are two ways to do this:
Use NumPy to generate the random value and put it in a tf.constant(), as you did in your question:
some_test = tf.constant(
np.random.normal(loc=0.0, scale=1.0, size=(2, 2)).astype(np.float32))
(Potentially faster, as it can use the GPU to generate the random numbers) Use TensorFlow to generate the random value and put it in a tf.Variable:
some_test = tf.Variable(
tf.random_normal([2, 2], mean=0.0, stddev=1.0, dtype=tf.float32)
sess.run(some_test.initializer) # Must run this before using `some_test`
I'm playing around with tensorflow and ran into a problem with the following code:
def _init_parameters(self, input_data, labels):
# the input shape is (batch_size, input_size)
input_size = tf.shape(input_data)[1]
# labels in one-hot format have shape (batch_size, num_classes)
num_classes = tf.shape(labels)[1]
stddev = 1.0 / tf.cast(input_size, tf.float32)
w_shape = tf.pack([input_size, num_classes], 'w-shape')
normal_dist = tf.truncated_normal(w_shape, stddev=stddev, name='normaldist')
self.w = tf.Variable(normal_dist, name='weights')
(I'm using tf.pack as suggested in this question, since I was getting the same error)
When I run it (from a larger script that invokes this one), I get this error:
ValueError: initial_value must have a shape specified: Tensor("normaldist:0", shape=TensorShape([Dimension(None), Dimension(None)]), dtype=float32)
I tried to replicate the process in the interactive shell. Indeed, the dimensions of normal_dist are unspecified, although the supplied values do exist:
In [70]: input_size.eval()
Out[70]: 4
In [71]: num_classes.eval()
Out[71]: 3
In [72]: w_shape.eval()
Out[72]: array([4, 3], dtype=int32)
In [73]: normal_dist.eval()
Out[73]:
array([[-0.27035281, -0.223277 , 0.14694688],
[-0.16527176, 0.02180306, 0.00807841],
[ 0.22624688, 0.36425814, -0.03099642],
[ 0.25575709, -0.02765726, -0.26169327]], dtype=float32)
In [78]: normal_dist.get_shape()
Out[78]: TensorShape([Dimension(None), Dimension(None)])
This is weird. Tensorflow generates the vector but can't say its shape. Am I doing something wrong?
As Ishamael says, all tensors have a static shape, which is known at graph construction time and accessible using Tensor.get_shape(); and a dynamic shape, which is only known at runtime and is accessible by fetching the value of the tensor, or passing it to an operator like tf.shape. In many cases, the static and dynamic shapes are the same, but they can be different - the static shape can be partially defined - in order allow the dynamic shape to vary from one step to the next.
In your code normal_dist has a partially-defined static shape, because w_shape is a computed value. (TensorFlow sometimes attempts to evaluate
these computed values at graph construction time, but it gets stuck at tf.pack.) It infers the shape TensorShape([Dimension(None), Dimension(None)]), which means "a matrix with an unknown number of rows and columns," because it knowns that w_shape is a vector of length 2, so the resulting normal_dist must be 2-dimensional.
You have two options to deal with this. You can set the static shape as Ishamael suggests, but this requires you to know the shape at graph construction time. For example, the following may work:
normal_dist.set_shape([input_data.get_shape()[1], labels.get_shape()[1]])
Alternatively, you can pass validate_shape=False to the tf.Variable constructor. This allows you to create a variable with a partially-defined shape, but it limits the amount of static shape information that can be inferred later on in the graph.
Similar question is nicely explained in TF FAQ:
In TensorFlow, a tensor has both a static (inferred) shape and a
dynamic (true) shape. The static shape can be read using the
tf.Tensor.get_shape method: this shape is inferred from the operations
that were used to create the tensor, and may be partially complete. If
the static shape is not fully defined, the dynamic shape of a Tensor t
can be determined by evaluating tf.shape(t).
So tf.shape() returns you a tensor, will always have a size of shape=(N,), and can be calculated in a session:
a = tf.Variable(tf.zeros(shape=(2, 3, 4)))
with tf.Session() as sess:
print sess.run(tf.shape(a))
On the other hand you can extract the static shape by using x.get_shape().as_list() and this can be calculated anywhere.
The variable can have a dynamic shape. get_shape() returns the static shape.
In your case you have a tensor that has a dynamic shape, and currently happens to hold value that is 4x3 (but at some other time it can hold a value with a different shape -- because the shape is dynamic). To set the static shape, use set_shape(w_shape). After that the shape you set will be enforced, and the tensor will be a valid initial_value.