This seems like an inconsistency in the TensorFlow API, since almost all other op functions accept NumPy arrays wherever a tf.Tensor is expected. I've filed an issue to track the fix.

Fortunately, there is a simple workaround, using tf.convert_to_tensor(). Replace your code with the following:

flipped_images = tf.image.random_flip_left_right(tf.convert_to_tensor(images))

The answer, as mentioned by the OP is to to use tensorflow2. Though it wasn't possible to get to the root cause of this problem it seems like it may be stemming from unsupported functionality in tensorflow1.x.

See here for the changes of 1.x vs 2.x

You are using the variable name image in your code, while you also import a module named image. This conflict results in trying to find img_to_array method in the variable rather than in the module.

Use a different name for you image variable, or directly import the img_to_array function:

from keras.preprocessing.image import img_to_array
[...]
x = img_to_array(img1)

Using .values on a pandas dataframe gives you a numpy array. This will not contain column names and such. You do this when setting X like this:

X = dataset[['Read?', 'x1', .. ,'x47']].values 

But then you try to get the column names from X (which it does not have) by writing X.columns here:

coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])

So store your column names in a variable or input them again, like this:

coeff_df = pd.DataFrame(regressor.coef_, ['Read?', 'x1', .. ,'x47'], columns=['Coefficient'])

This is more of a programming question than a data science question and is therefore better suited for stackoverflow. The vectorizer is trying to convert the string values to lowercase using the .lower() method. However, you are passing in a numpy array, which does not have this method. Instead of passing in a numpy array, you can simply use a pandas dataframe (which does have the .lower() method, see the documentation) by removing the .values attribute in dataset.iloc[:,:-1].values.

Check the shape of mealarray. If the argument to fit_transform is an array of strings, it must be a one-dimensional array. (That is, mealarray.shape must be of the form (n,).) For example, you'll get the "no attribute" error if mealarray has a shape such as (n, 1).

You could try something like

data = vectorizer.fit_transform(mealarray.ravel())

Use numpy.array to use shape attribute.

>>> import numpy as np
>>> X = np.array([
...     [[-9.035250067710876], [7.453250169754028], [33.34074878692627]],
...     [[-6.63700008392334], [5.132999956607819], [31.66075038909912]],
...     [[-5.1272499561309814], [8.251499891281128], [30.925999641418457]]
... ])
>>> X.shape
(3L, 3L, 1L)

NOTE X.shape returns 3-items tuple for the given array; [n, T] = X.shape raises ValueError.