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Tensorflow - ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float)

Continuation from previous question: Tensorflow - TypeError: 'int' object is not iterable

My training data is a list of lists each comprised of 1000 floats. For example, x_train[0] =

[0.0, 0.0, 0.1, 0.25, 0.5, ...]

Here is my model:

model = Sequential()

model.add(LSTM(128, activation='relu',
               input_shape=(1000, 1), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))

opt = tf.keras.optimizers.Adam(lr=1e-3, decay=1e-5)

model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=3, validation_data=(x_test, y_test))

Here is the error I'm getting:

Traceback (most recent call last):
      File "C:\Users\bencu\Desktop\ProjectFiles\Code\Program.py", line 88, in FitModel
        model.fit(x_train, y_train, epochs=3, validation_data=(x_test, y_test))
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 728, in fit
        use_multiprocessing=use_multiprocessing)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 224, in fit
        distribution_strategy=strategy)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 547, in _process_training_inputs
        use_multiprocessing=use_multiprocessing)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 606, in _process_inputs
        use_multiprocessing=use_multiprocessing)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\data_adapter.py", line 479, in __init__
        batch_size=batch_size, shuffle=shuffle, **kwargs)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\data_adapter.py", line 321, in __init__
        dataset_ops.DatasetV2.from_tensors(inputs).repeat()
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\data\ops\dataset_ops.py", line 414, in from_tensors
        return TensorDataset(tensors)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\data\ops\dataset_ops.py", line 2335, in __init__
        element = structure.normalize_element(element)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\data\util\structure.py", line 111, in normalize_element
        ops.convert_to_tensor(t, name="component_%d" % i))
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1184, in convert_to_tensor
        return convert_to_tensor_v2(value, dtype, preferred_dtype, name)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1242, in convert_to_tensor_v2
        as_ref=False)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1296, in internal_convert_to_tensor
        ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\framework\tensor_conversion_registry.py", line 52, in _default_conversion_function
        return constant_op.constant(value, dtype, name=name)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\framework\constant_op.py", line 227, in constant
        allow_broadcast=True)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\framework\constant_op.py", line 235, in _constant_impl
        t = convert_to_eager_tensor(value, ctx, dtype)
      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\framework\constant_op.py", line 96, in convert_to_eager_tensor
        return ops.EagerTensor(value, ctx.device_name, dtype)
    ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float).

I've tried googling the error myself, I found something about using the tf.convert_to_tensor function. I tried passing my training and testing lists through this but the function won't take them.

What outputs do you get for the following?: (1) print(len(x_train)); (2) print(len(x_train[0])); (3) print(x_train.shape); (4) print(x_train[0].shape). If error, just skip the number
More importantly, it'd help to see your full code, as I cannot reproduce the issue with information provided. I suspect you're using variable input sizes, or your x_train list dimensions are inconsistent; what's the output for for seq in x_train: print(np.array(seq).shape)? Can share here
@OverLordGoldDragon - print(len(x_train)) outputs 13520, print(len(x_train[0])) outputs 1000, and the for loop outputs (1000,) for every single value in x_train.
What does the following output? import sys; import tensorflow as tf; import keras; print(sys.version); print(tf.__version__); print(keras.__version__) # python ver, tf ver, keras ver Also, are you able to share a subset of your data, via e.g. Dropbox?

O
OverLordGoldDragon

TL;DR Several possible errors, most fixed with x = np.asarray(x).astype('float32').

Others may be faulty data preprocessing; ensure everything is properly formatted (categoricals, nans, strings, etc). Below shows what the model expects:

[print(i.shape, i.dtype) for i in model.inputs]
[print(o.shape, o.dtype) for o in model.outputs]
[print(l.name, l.input_shape, l.dtype) for l in model.layers]

The problem's rooted in using lists as inputs, as opposed to Numpy arrays; Keras/TF doesn't support former. A simple conversion is: x_array = np.asarray(x_list).

The next step's to ensure data is fed in expected format; for LSTM, that'd be a 3D tensor with dimensions (batch_size, timesteps, features) - or equivalently, (num_samples, timesteps, channels). Lastly, as a debug pro-tip, print ALL the shapes for your data. Code accomplishing all of the above, below:

Sequences = np.asarray(Sequences)
Targets   = np.asarray(Targets)
show_shapes()

Sequences = np.expand_dims(Sequences, -1)
Targets   = np.expand_dims(Targets, -1)
show_shapes()
# OUTPUTS
Expected: (num_samples, timesteps, channels)
Sequences: (200, 1000)
Targets:   (200,)

Expected: (num_samples, timesteps, channels)
Sequences: (200, 1000, 1)
Targets:   (200, 1)

As a bonus tip, I notice you're running via main(), so your IDE probably lacks a Jupyter-like cell-based execution; I strongly recommend the Spyder IDE. It's as simple as adding # In[], and pressing Ctrl + Enter below:

Function used:

def show_shapes(): # can make yours to take inputs; this'll use local variable values
    print("Expected: (num_samples, timesteps, channels)")
    print("Sequences: {}".format(Sequences.shape))
    print("Targets:   {}".format(Targets.shape))   

I faced this issue when I inserted to a 3D numpy array to a pandas dataframe and then fetched numpy array from dataframe to create tensor object. To resolve this, I directly passed numpy array to create tensor object.
how can I fix that if my ytrain is an array of multi-label classes? (1000,20) 1000 records with 20 labels for each record?
V
Vasco Cansado Carvalho

After trying everything above with no success, I found that my problem was that one of the columns from my data had boolean values. Converting everything into np.float32 solved the issue!

import numpy as np

X = np.asarray(X).astype(np.float32)

Yup this shorty answer was everything needed, thanks a lot! :)
THIS DESERVE TO BE THE APPROVED ANSWER TO THIS TREAD
great answer, my data was object instead of float32
S
Scott

This should do the trick:

x_train = np.asarray(x_train).astype(np.float32)
y_train = np.asarray(y_train).astype(np.float32)

Z
Zoltan Fedor

This is a HIGHLY misleading error, as this is basically a general error, which might have NOTHING to do with floats.

For example in my case it was caused by a string column of the pandas dataframe having some np.NaN values in it. Go figure!

Fixed it by replacing them with empty strings:

df.fillna(value='', inplace=True)

Or to be more specific doing this ONLY for the string (eg 'object') columns:

cols = df.select_dtypes(include=['object'])
for col in cols.columns.values:
    df[col] = df[col].fillna('')

np.nan is a float, so the nan is misleading you, not the error.
it worked nicely and smooth
I
Igor

I had many different inputs and target variables and didn't know which one was causing the problem.

To find out on which variable it breaks you can add a print value in the library package using the path is specified in your stack strace:

      File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\framework\constant_op.py", line 96, in convert_to_eager_tensor
        return ops.EagerTensor(value, ctx.device_name, 

Adding a print statement in this part of the code allowed me to see which input was causing the problem:

constant_op.py:

  ....
      dtype = dtype.as_datatype_enum
    except AttributeError:
      dtype = dtypes.as_dtype(dtype).as_datatype_enum
  ctx.ensure_initialized()
  print(value) # <--------------------- PUT PRINT HERE
  return ops.EagerTensor(value, ctx.device_name, dtype)

After observing which value was problematic conversion from int to astype(np.float32) resolved the problem.


D
Danilo Soto Villena

Try with it for convert np.float32 to tf.float32 (datatype that read keras and tensorflow):

tf.convert_to_tensor(X_train, dtype=tf.float32)


r
rosyaniv

Could also happen due to a difference in versions (I had to move back from tensorflow 2.1.0 to 2.0.0.beta1 in order to solve this issue).


same here, I am trying to figure out what have been changed. Worked fine at version 1 too.
A
Alexander

You may want to check data types in input data set or array and than convert it to float32:

train_X[:2, :].view()
#array([[4.6, 3.1, 1.5, 0.2],
#       [5.9, 3.0, 5.1, 1.8]], dtype=object)
train_X = train_X.astype(np.float32)
#array([[4.6, 3.1, 1.5, 0.2],
#       [5.9, 3. , 5.1, 1.8]], dtype=float32)

N
Newt

You'd better use this, it is because of the uncompatible version of keras

from keras import backend as K
X_train1 = K.cast_to_floatx(X_train)
y_train1 = K.cast_to_floatx(y_train)

a
amalik2205

Use this if you are using a DataFrame and has multiple columns type:

numeric_list = df.select_dtypes(include=[np.number]).columns
df[numeric_list] = df[numeric_list].astype(np.float32)

Would numpy.float64 work as good in this case as well?
A
Adelson Araújo

In my case, it didn't work to cast to np.float32.

For me, everything ran normally during training (probably because I was using tf.data.Dataset.from_generator as input for fit()), but when I was trying to call predict() on 1 instance (using a np.array), the error shows up.

As a solution, I had to reshape the array x_array.reshape(1, -1) before calling predict and it worked.


Y
Yuriy Sereda

I avoided this problem by enforcing floating-point format during data import:

df = pd.read_csv('titanic.csv', dtype='float')


d
drew_is_good

Just had the same issue and it ended up being because I was trying to pass an array of array objects, not an array of arrays as expected. Hope this helps someone in the future!


H
Harsh Suvarna

try

X_train =t ensorflow.convert_to_tensor(X_train, dtype=tensorflow.float32)
y_train = tensorflow.convert_to_tensor(y_train, dtype=tensorflow.float32)
X_test = tensorflow.convert_to_tensor(X_test, dtype=tensorflow.float32)
y_test = tensorflow.convert_to_tensor(y_test, dtype=tensorflow.float32)

As it’s currently written, your answer is unclear. Please edit to add additional details that will help others understand how this addresses the question asked. You can find more information on how to write good answers in the help center.
N
Nitin Yadav

As answered by most people above, converting data in np.float32 / float32 by various means already told here.

While doing so if you get another error which is "ValueError: setting an array element with a sequence".

In that case, try converting your data into type list and then converting it into type tensor as you were trying earlier.


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