未能找到可以处理输入的数据适配器：<class 'numpy.ndarray'> , (<class 'list'> 包含类型 {“<class 'int'> ”})</class></class></class>

Question

history = model.fit(X, y, batch_size=32, epochs=40, validation_split=0.1)

the line problem was this

Showing error:

ValueError: Failed to find data adapter that can handle input: <class 'numpy.ndarray'>, (<class 'list'> containing values of types {"<class 'int'>"})

Answer 1

I was facing the same issue. Turns out it was a in the form of a list. I had to convert the fields into a numpy array like:

training_padded = np.array(training_padded)
training_labels = np.array(training_labels)
testing_padded = np.array(testing_padded)
testing_labels = np.array(testing_labels)

thats it!

Answer 2

ValueError in TensorFlow

https://pythonprogramming.net/convolutional-neural-network-deep-learning-python-tensorflow-keras/

I tried following code and worked for me:

IMG_SIZE = 50

X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)

y = np.array(y)

history = model.fit(X, y, batch_size=32, epochs=40, validation_split=0.1)

Answer 3

So this is happening the the newer version of tensorflow I'm not sure from where but I was on version 2.0.0 and this same thing happened

I'm assuming that you are only converting the X array into a numpy array But rather try converting 'X' as well as 'y' to numpy array using the dtype as np.uint8

That should resolve the problem

Answer 4

In my case the problem was only in y. it was a list. in that case i had to change

y = np.array(y)

Answer 5

VIKI already said a good answer. I am adding more information. It used to crash colab host for me as well, before I added the np.array() wrappers.

# Need to call np.array() around pandas dataframes.
# This crashes the colab host from TF attempting a 32GB memory alloc when np.array() wrappers are not used around pandas dataframes.
# Wrapping also cures warning about "Failed to find data adapter that can handle input"
history = model.fit(x=np.array(tr_X), y=np.array(tr_Y), epochs=3, validation_data=(np.array(va_X), np.array(va_Y)), batch_size=batch_size, steps_per_epoch=spe, validation_freq=5)

Crashing host due to out of memory problem has something to do with this:

Tensorflow dense gradient explanation?

Answer 6

Mahmud's answer fixes the TensorFlow Tutorial "Basic regression: Predict fuel efficiency" error in section [30]. These are the 2 lines:

Change this:

example_batch = normed_train_data[:10]
example_result = model.predict(example_batch)

To this:

example_batch = np.array(normed_train_data[0:10]) 
example_result = model.predict(example_batch)

Thanks Mahmud

Answer 7

For those who encounter this issue:

Check the type before you throw your data into the model. For example, it should be

print(type(X)) 
# numpy.ndarray

Instead of

print(type(X))
# list

Simply transform the X by

import numpy as np
X = np.array(X)

Answer 8

Just type cast the arrays.

for example:

import numpy as np
features = np.array(features,dtype='float64')
labels = np.array(labels, dtype ='float64')

Answer 9

you need to convert X and y like this:

X = np.array(X).reshape(-1, 50, 50, 1)

and for y

y=np.array(y)

Answer 10

problem with the below line:

model.fit(X, y, batch_size=32, epochs=3, validation_split=0.3)

showing error: Failed to find data adapter that can handle input: , ( containing values of types {""})

Please help me how to sort out the issues