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

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()

# OUTPUTSExpected: (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))   

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

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('')