Я пытался сделать простой проект, такой как "Mnist", но я нашел ошибку о несоответствии плотных
я пытался использовать другой набор данных из https://github.com/amir-saniyan/HodaDatasetReader
и пытался последовать за мной до "мнист"
но эта ошибка произошла!
Traceback (most recent call last):
File "train..py", line 51, in <module>
score = network.evaluate(x_test, y_test)
File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1102, in evaluate
batch_size=batch_size)
File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 789, in _standardize_user_data
exception_prefix='target')
File "/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py", line 128, in standardize_input_data
'with shape ' + str(data_shape))
ValueError: Error when checking target: expected dense_2 to have 2 dimensions, but got array with shape (20000, 10, 2)
и ошибка в этой строке:
score = network.evaluate(x_test, y_test)"
как я определяю свои data_Set:
import numpy as np
import matplotlib.pyplot as plt
from HodaDatasetReader.HodaDatasetReader import read_hoda_cdb, read_hoda_dataset
plt.rcParams['figure.figsize'] = (7,9) # Make the figures a bit bigger
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.utils import np_utils, to_categorical
загрузка данных тренировки
nb_classes = 10
x_train, y_train = read_hoda_dataset(dataset_path='HodaDatasetReader/DigitDB/Train 60000.cdb',
images_height=32,
images_width=32,
one_hot=False,
reshape=True)
x_test, y_test = read_hoda_dataset(dataset_path='HodaDatasetReader/DigitDB/Test 20000.cdb',
images_height=32,
images_width=32,
one_hot=True,
reshape=False)
x_train = x_train.reshape((60000, 32 * 32))
x_train = x_train.astype('float32') / 255
x_test = x_test.reshape((20000, 32 * 32))
x_test = x_test.astype('float32') / 255
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
модель
network = Sequential()
network.add(Dense(512, activation='relu', input_shape=(32 * 32,)))
network.add(Dense(10, activation='softmax'))
и прогноз
network.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
network.fit(x_train, y_train, epochs=1, batch_size=1000)
score = network.evaluate(x_test, y_test)###error was here :(