'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.

Solution 1:^[1]

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))

Solution 2:^[2]

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)

Solution 3:^[3]

This should do the trick:

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

Solution 4:^[4]

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('')

Solution 5:^[5]

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)

Solution 6:^[6]

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).

Solution 7:^[7]

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.

Solution 8:^[8]

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)

Solution 9:^[9]

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)

Solution 10:^[10]

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)

Solution 11:^[11]

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.

Solution 12:^[12]

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

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

Solution 13:^[13]

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!

Solution 14:^[14]

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)

Sources

This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Source: Stack Overflow

Solution	Source
Solution 1
Solution 2	Vasco Cansado Carvalho
Solution 3	Scott
Solution 4
Solution 5	Danilo Soto Villena
Solution 6	rosyaniv
Solution 7
Solution 8	Alexander
Solution 9	Newt
Solution 10	amalik2205
Solution 11
Solution 12	Yuriy Sereda
Solution 13	drew_is_good
Solution 14	Harsh Suvarna

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

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