试图理解数据库非规范化，这个数据库是非规范化的吗？

Question

我一直在努力寻找在Firebase上设计大型数据集数据库的最佳方法几天，我甚至在database administration网站上写了一个问题。

我想出了一个设计，我不知道那些被称为非规范化数据的东西。我想尽量减少数据的查询时间，也不要那么难以插入/更新数据。

这是我的设计：

这种数据的数据库设计是否合适？ （请在数据库管理网站上查看我的question，了解有关数据性质的更多详细信息）。 但这里也是对数据性质的简短描述：

  

所以我有一个affiliator_category，可能是银行，俱乐部或组织。每个类别都包含多个affiliators，每个关联者包含的stores数量分为store_category，每个商店的数量为offers。   而对于用户方（购物者）。 users在memberships个affiliators中有多个spendings，而他/她的数量为import os import sys import tensorflow as tf import Input import os, re FLAGS = tf.app.flags.FLAGS TOWER_NAME = 'tower' tf.app.flags.DEFINE_integer('batch_size', 1, "hello") tf.app.flags.DEFINE_string('data_dir', '/home/zan/Desktop/Neural-Network-Prostate', "hello") NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = Input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = Input.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL NUM_EPOCHS_PER_DECAY = 3 MOVING_AVERAGE_DECAY = 0.9999 NUM_EPOCHS_PER_DECAY = 30 LEARNING_RATE_DECAY_FACTOR = 0.1 INITIAL_LEARNING_RATE = 0.1 def _activation_summary(x): tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) tf.histogram_summary(tensor_name + '/activations', x) tf.scalar_summary(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) def inputs(): if not FLAGS.data_dir: raise ValueError('Source Data Missing') data_dir = FLAGS.data_dir images, labels = Input.inputs(data_dir = data_dir, batch_size = FLAGS.batch_size) return images, labels def eval_inputs(): data_dir = FLAGS.data_dir images, labels = Input.eval_inputs(data_dir = data_dir, batch_size = 1) return images, labels def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape = shape) return tf.Variable(initial) def conv2d(images, W): return tf.nn.conv2d(images, W, strides = [1, 1, 1, 1], padding = 'SAME') def max_pool_5x5(images): return tf.nn.max_pool(images, ksize = [1, 5, 5, 1], strides = [1, 5, 5, 1], padding = 'SAME') def forward_propagation(images): with tf.variable_scope('conv1') as scope: W_conv1 = weight_variable([5, 5, 3, 32]) b_conv1 = bias_variable([32]) image_matrix = tf.reshape(images, [-1, 1750, 1750, 3]) h_conv1 = tf.nn.sigmoid(conv2d(image_matrix, W_conv1) + b_conv1) _activation_summary(h_conv1) h_pool1 = max_pool_5x5(h_conv1) with tf.variable_scope('conv2') as scope: W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.sigmoid(conv2d(h_pool1, W_conv2) + b_conv2) _activation_summary(h_conv2) h_pool2 = max_pool_5x5(h_conv2) with tf.variable_scope('conv3') as scope: W_conv3 = weight_variable([5, 5, 64, 128]) b_conv3 = bias_variable([128]) h_conv3 = tf.nn.sigmoid(conv2d(h_pool2, W_conv3) + b_conv3) _activation_summary(h_conv3) h_pool3 = max_pool_5x5(h_conv3) with tf.variable_scope('local3') as scope: W_fc1 = weight_variable([14 * 14 * 128, 256]) b_fc1 = bias_variable([256]) h_pool3_flat = tf.reshape(h_pool3, [-1, 14 * 14 * 128]) h_fc1 = tf.nn.sigmoid(tf.matmul(h_pool3_flat, W_fc1) + b_fc1) _activation_summary(h_fc1) keep_prob = tf.Variable(1.0) W_fc2 = weight_variable([256, 4]) b_fc2 = bias_variable([4]) y_conv = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2) _activation_summary(y_conv) return y_conv def error(forward_propagation_results, labels): labels = tf.one_hot(labels, 4) tf.transpose(labels) labels = tf.cast(labels, tf.float32) mean_squared_error = tf.square(tf.sub(labels, forward_propagation_results)) cost = tf.reduce_mean(mean_squared_error) #train_loss = tf.train.GradientDescentOptimizer(learning_rate = 0.05).minimize(cost) tf.histogram_summary('accuracy', mean_squared_error) tf.add_to_collection('losses', cost) tf.scalar_summary('LOSS', cost) return cost def _add_loss_summaries(cost): loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') losses = tf.get_collection('LOSS') loss_averages_op = loss_averages.apply(losses + [cost]) for l in losses + [cost]: tf.scalar_summary(l.op.name +' (raw)', l) tf.scalar_summary(l.op.name, loss_averages.average(l)) return loss_averages_op def train(cost, global_step): num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY) lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE, global_step, decay_steps, LEARNING_RATE_DECAY_FACTOR, staircase=True) tf.scalar_summary('learning_rate', lr) loss_averages_op = _add_loss_summaries(cost) with tf.control_dependencies([loss_averages_op]): opt = tf.train.GradientDescentOptimizer(lr) grads = opt.compute_gradients(cost) apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) for var in tf.trainable_variables(): tf.histogram_summary(var.op.name, var) for grad, var in grads: if grad is not None: tf.histogram_summary(var.op.name + '/gradients', grad) variable_averages = tf.train.ExponentialMovingAverage( MOVING_AVERAGE_DECAY, global_step) variables_averages_op = variable_averages.apply(tf.trainable_variables()) with tf.control_dependencies([apply_gradient_op, variables_averages_op]): train_op = tf.no_op(name='train') return train_op 。