Проблемы с моделью keras / tensorflow - PullRequest
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Проблемы с моделью keras / tensorflow

0 голосов
/ 21 июня 2020

Я генерирую модель машинного обучения, которая предсказывает состояние машины. Эта машина следует следующему уравнению: A x + B y = x_next, где x, y и x_next - это векторы 1x4. Итак, моя модель ML получает x и y в качестве входов и выходов x_next. Но во время обучения у меня возникла проблема. 

data_chosen = random.sample(state_input_next_state_list, int(len(state_input_next_state_list)* 0.8 ))
#x_train = list(map(lambda x: np.array(x[0] + x[1]), data_chosen))
x_data = list(map(lambda x: np.vstack((x[0], x[1])) , data_chosen))
y_data = list(map(lambda x: np.array(x[2]), data_chosen))

print(x_data[0])
print(x_data[0].shape)
print(y_data[0])
print(type(x_data[0]))

# [[-0.10094348 -0.96692593  1.16288356 -1.39277914]
# [ 0.          0.00338941  0.         -0.00338941]]
# (2, 4)
# [-0.11705892 -0.97656013  1.13967058 -1.37345424]
# <class 'numpy.ndarray'>

Обратите внимание на результат оператора печати. ​​

model = Sequential()

model.add(Dense(max(x_data[0].shape), input_shape=x_data[0].shape, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(y_data[0]), activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])



model.fit(x_data, y_data,
          epochs=20,
          batch_size=len(y_data))

я получаю ошибку 


---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-105-f8499d1a6973> in <module>
      1 model.fit(x_data, y_data,
      2           epochs=20,
----> 3           batch_size=len(y_data))

c:\software\python37\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
   1152             sample_weight=sample_weight,
   1153             class_weight=class_weight,
-> 1154             batch_size=batch_size)
   1155 
   1156         # Prepare validation data.

c:\software\python37\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    577             feed_input_shapes,
    578             check_batch_axis=False,  # Don't enforce the batch size.
--> 579             exception_prefix='input')
    580 
    581         if y is not None:

c:\software\python37\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    107                 'Expected to see ' + str(len(names)) + ' array(s), '
    108                 'but instead got the following list of ' +
--> 109                 str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
    110         elif len(names) > 1:
    111             raise ValueError(

ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 array(s), but instead got the following list of 3 arrays: [array([[-0.19053051, -0.38436736,  0.35624974, -0.04435445],
       [ 0.        ,  0.00178595,  0.        , -0.00178595]]), array([[-3.92162966e-01,  6.14237515e-01, -6.34753706e-01,
         1.04811...

​

1 Ответ

0 голосов
/ 22 июня 2020

Я нашел решение своей проблемы 

n = 100
states = [random_state() for _ in range(n) ]
inputs = [random_input() for _ in range(n) ]
state_input_product = list(itertools.product(states,inputs))
print(len(state_input_product))
#next_states = list(map(next_state, state_input_product))
state_input_next_state_list = list(map(bundle_state_input_next_state, state_input_product))

print(state_input_next_state_list[0])


# In[82]:


train_chosen = random.sample(state_input_next_state_list, int(len(state_input_next_state_list)* 0.8 ))
test_chosen = random.sample(state_input_next_state_list, int(len(state_input_next_state_list)* 0.2 ))
x_train = np.stack(list(map(lambda x: np.concatenate((x[0], x[1])) , train_chosen)))
y_train = np.stack(list(map(lambda x: np.array(x[2]), train_chosen)))
x_test = np.stack(list(map(lambda x: np.concatenate((x[0], x[1])) , test_chosen)))
y_test = np.stack(list(map(lambda x: np.array(x[2]), test_chosen)))


# In[93]:


model = Sequential()

model = tf.keras.Sequential(
  [
      tf.keras.layers.Input(shape=(8), name='INPUT'),
      tf.keras.layers.Dense(32, activation='softmax', name='middle'),
      tf.keras.layers.Dense(len(y_data[0]), activation='softmax', name='OUTPUT')
  ])

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.summary()


# In[91]:


batch_size = 100
epochs = 10

model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)


# In[92]:


score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])


# In[ ]:
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