["python","tensorflow","keras","lstm"]
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.
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))