from sklearn.metrics import f1_score, precision_score, recall_score
from keras.models import Sequential
from keras.layers import Activation
from keras.layers import Dense, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.callbacks import Callback
import keras
from keras.datasets import cifar10
import numpy as np
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train1 = X_train.copy().ravel()
y_train1 = y_train.copy().ravel()
X_train2 = np.resize(X_train1, 64*64*500)
y_train2 = np.resize(y_train1, 64*64*500)
X_train = X_train2.reshape((-1, 64, 64, 1))
y_train = y_train2.reshape((-1, 64, 64, 1))
metrics = Metrics()
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 1)))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss=keras.losses.binary_crossentropy,
optimizer=keras.optimizers.Adam(),
)
model.fit(X_train, y_train, epochs=20, batch_size=1024, verbose=1, validation_data=(X_test, y_test), callbacks=[metrics])
model.save('bushtranser.model')
我是这样的人 . ValueError：检查目标时出错：预期activation_4有2个维度，但得到的数组有形状（500,64,64,1） . 如何解决这个问题？最小的改变将解决这个问题，现在并不是真的担心模型性能 .
1 回答
我可以问你，你想要什么？使用500个数据调整形状（32,32,1）到（64,64,1）的火车模型？