Блог

Я работаю над CNN, но я продолжаю сталкиваться с этой ошибкой:

 ValueError: Shapes (None, 3) and (None, 30, 30, 3) are incompatible
 

Мои y_train и y_test являются одним горячим закодированным, но это не оказывает никакого влияния.

Есть какие-нибудь советы?

 import numpy as np
from numpy import random
import tensorflow as tf
from tensorflow.keras.models import Sequential
#from tensorflow.keras.utils import to_categorical
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
import pickle
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
from tensorflow.keras.optimizers import RMSprop
import matplotlib.pyplot as plt
import cv2
import os

X_train = pickle.load(open("X_train.pickle","rb"))
y_train = pickle.load(open("y_train.pickle","rb"))
#y_train = np.array(y_train)
y_train = tf.keras.utils.to_categorical(y_train, num_classes=3)

X_test = pickle.load(open("X_test.pickle","rb"))
y_test = pickle.load(open("y_test.pickle","rb"))
#y_test = np.array(y_test)
y_test = tf.keras.utils.to_categorical(y_train, num_classes=3)

model = Sequential()

model.add(Dense(64, activation='relu',input_shape = X_train.shape[1:]))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3, 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_train, y_train,
         epochs=2000,
         batch_size=128,
         validation_split = 0.1)