Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error **Tensorflow – ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float)** **in python**. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

Table of Contents

## How Tensorflow – ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float) Error Occurs?

Today I get the following error **Tensorflow – ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float)** **in python**.

## How To Solve Tensorflow – ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float) Error ?

**How To Solve Tensorflow – ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float) Error ?**To Solve Tensorflow – ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float) Error 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!**Tensorflow – ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float)**To Solve Tensorflow – ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float) Error 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!

## Solution 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

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)

**Summery**

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.

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