Tensorflow Tensor out of CSV has no size? - tensorflow

Question

I just can't get any dimensions (size, lenght) out of this damn tensor "datatens". here is the code and the error message:
import tensorflow as tf
import numpy as np
import tflearn
import pandas as pd
from tensorflow import keras
file = 'some.csv'
record_defaults = [tf.float64]*18
from tflearn.data_utils import load_csv
data , label = load_csv(file, target_column=0,has_header=True,
categorical_labels=True, n_classes=50)
datatens = tf.data.Dataset.from_tensor_slices((data,label))
print(datatens.get_shape().as_list())
ERROR:
<TensorSliceDataset shapes: ((17,), (50,)), types: (tf.string, tf.float64)>
Traceback (most recent call last):
File "basic_class.m", line 44, in <module>
print(datatens.get_shape().as_list())
AttributeError: 'TensorSliceDataset' object has no attribute 'get_shape'
FOLLOWUP:
after getting eager execution running im curious, why my tensor is integer instead of float. here is the output of the advised code.
CODE:
print(tf.shape(data))
print(tf.shape(label))
OUTPUT:
Tensor("Shape:0", shape=(2,), dtype=int32)
Tensor("Shape_1:0", shape=(2,), dtype=int32)

Answer 1

When you call tf.data.Dataset.from_tensor_slices, you get a dataset, not a tensor. A dataset is essentially a container of tensors, and you can access its tensors in a few ways.
The simplest way is to call the dataset's make_one_shot_iterator method. This returns an iterator that cycles through the tensors. The best documentation on datasets and iterators is here.
Are you sure you want to call tf.data.Dataset.from_tensor_slices? Aren't data and label already tensors?
EDIT:
If you want to validate the tensor containing labels, try this code:
import tensorflow as tf
import numpy as np
import tflearn
import pandas as pd
from tensorflow import keras
from tflearn.data_utils import load_csv
tf.enable_eager_execution()
file = 'some.csv'
record_defaults = [tf.float64]*18
data, label = load_csv(file, target_column=0,has_header=True,
categorical_labels=True, n_classes=50)
print(tf.shape(label))
Enabling eager execution is important because it lets you access the tensor without having to create and run a session.