这是我第一次使用sequelize，我希望运行多对多的迁移。这是我的模型的设置：
'use strict';
module.exports = function(sequelize, DataTypes) {
var Collection = sequelize.define('Collection', {
title: DataTypes.STRING
}, {
classMethods: {
associate: function(models) {
// associations can be defined here
Collection.belongsToMany(Post, { through: CollectionPost, foreignKey: 'collection_id' });
}
}
});
return Collection;
};
models/post.js和'use strict';
module.exports = function(sequelize, DataTypes) {
var Post = sequelize.define('Post', {
title: DataTypes.STRING
}, {
classMethods: {
associate: function(models) {
// associations can be defined here
Post.belongsToMany(Collection, { through: CollectionPost, foreignKey: 'post_id' });
}
}
});
return Post;
};
sync我知道sequelize有const models = require('../models');
function test() {
return models.sequelize.sync((err, response) => {
console.log(err);
console.log('.........');
console.log(response);
});
}
test();
方法可以将模型与迁移同步。但是我只想同步一次，因此我写了这个小脚本：
CollectionPost但是，这不是创建# Step 2: create placeholders for input X (Features) and label Y (binary result)
X = tf.placeholder(tf.float32, shape=[None, 9], name="X")
Y = tf.placeholder(tf.float32, shape=[None,2], name="Y")
# Step 3: create weight and bias, initialized to 0
w = tf.Variable(tf.truncated_normal([9, 2]), name="weights")
b = tf.Variable(tf.zeros([1,2]), name="bias")
# Step 4: logistic multinomial regression / softmax
score = tf.matmul(X, w) + b
# Step 5: define loss function
entropy = tf.nn.softmax_cross_entropy_with_logits(logits=score, labels=Y, name="entropy")
regularizer = tf.nn.l2_loss(w)
loss = tf.reduce_mean(entropy + BETA * regularizer, name="loss")
# Step 6: using gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate=LEARNING_RATE).minimize(loss)
# Step 7: Prediction
Y_predicted = tf.nn.softmax(tf.matmul(X, w) + b)
correct_prediction = tf.equal(tf.argmax(Y_predicted,1), tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
表。我错过了什么？