使用executeSQL（）在Filemaker中填充门户

Question

我有一个带有Filemaker 16应用程序的客户端，该应用程序使用与SQL Server数据库的ODBC连接。使用Filemaker，我所知道的关于数据库的一切似乎都是错误的。我已经用这个摔了2天了，只是作为最后的手段问一下---是否可以使用SQL查询的结果填充门户字段？我试图返回唯一的记录并排除重复。 executeSQL（）调用将在哪里进行？我想也许在门户网站上的过滤器中，但事实并非如此。对不起基本问题。

Answer 1

FileMaker门户将始终在关系图中显示与给定关系匹配的记录。这组记录可以通过门户过滤器进一步约束。

要获得唯一项目列表，您需要在相关表格中创建一个计算字段，用于确定记录是否唯一。这可以通过多种方式完成，ExecuteSQL就是其中之一。然后，您可以将此字段用作关系本身的谓词，或在门户网站过滤器中使用它。

Answer 2

这只是一种方法。

在要显示记录的（影子）表中，如果要包含记录，则创建一个返回ONE的计算字段，如果应该忽略则创建1。它可能是这样的：

ONE

现在，您要在表中显示一个门户网站，创建一个全局计算字段LAYOUT_CONTEXT，返回SHADOW_TABLE，然后在该表与您要查看门户网站的表之间创建关系使用import okhttp3.Request; public interface OkHttp3RequestBuilder { Request create(HttpServletRequest request); } 和上面的SQL calc作为匹配字段。

在上面有 final HttpUrl targetUrl = HttpUrl.get("http://internal.xyz.com"); final RequestBody requestBody = // ????? final Request httpRequest = new Request.Builder() .post(requestBody) .url(targetUrl) .build(); return httpRequest; 表的上下文的布局中，您可以创建一个from __future__ import print_function from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf import datetime def average_gradients(tower_grads): average_grads = [] for grad_and_vars in zip(*tower_grads): # Note that each grad_and_vars looks like the following: # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) grads = [] for g, _ in grad_and_vars: # Add 0 dimension to the gradients to represent the tower. expanded_g = tf.expand_dims(g, 0) # Append on a 'tower' dimension which we will average over below. grads.append(expanded_g) # Average over the 'tower' dimension. grad = tf.concat(axis=0, values=grads) grad = tf.reduce_mean(grad, 0) # Keep in mind that the Variables are redundant because they are shared # across towers. So .. we will just return the first tower's pointer to # the Variable. v = grad_and_vars[0][1] grad_and_var = (grad, v) average_grads.append(grad_and_var) return average_grads with tf.device('/cpu:0'): x = tf.placeholder(tf.float32, [None, 784], name='x') x_img=tf.reshape(x, [-1, 28, 28, 1]) x_dict={} x_dict['x0'],x_dict['x1'] = tf.split(x_img,2) y_dict={} y = tf.placeholder(tf.float32, [None, 10], name='y') y_dict['y0'],y_dict['y1'] = tf.split(y,2) opt=tf.train.GradientDescentOptimizer(0.01) keep_prob = tf.placeholder(tf.float32) w0=tf.get_variable('w0',initializer=tf.truncated_normal([5, 5,1,32], stddev=0.1)) b0=tf.get_variable('b0',initializer=tf.zeros([32])) w1=tf.get_variable('w1',initializer=tf.truncated_normal([5,5,32,64], stddev=0.1)) b1=tf.get_variable('b1',initializer=tf.zeros([64])) w2=tf.get_variable('w2',initializer=tf.truncated_normal([7*7*64,1024], stddev=0.1)) b2=tf.get_variable('b2',initializer=tf.zeros([1024])) w3=tf.get_variable('w3',initializer=tf.truncated_normal([1024,10], stddev=0.1)) b3=tf.get_variable('b3',initializer=tf.zeros([10])) grads=[] def conv2d(xx, W): return tf.nn.conv2d(xx, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(xx): return tf.nn.max_pool(xx, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') def model_forward(xx): h_conv1=tf.nn.relu(conv2d(xx,w0)+b0); h_pool1=max_pool_2x2(h_conv1) h_conv2=tf.nn.relu(conv2d(h_pool1,w1)+b1); h_pool2=max_pool_2x2(h_conv2) h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,w2)+b2) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) y = tf.nn.sigmoid(tf.matmul(h_fc1_drop,w3)+b3) return y for i in range(0,2): with tf.device(('/gpu:{0}').format(i)): with tf.variable_scope(('scope_gpu_{0}').format(i)): yy=model_forward(x_dict[('x{0}').format(i)]) cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_dict[('y{0}').format(i)] * tf.log(yy), reduction_indices=[1])) grads.append(opt.compute_gradients(cross_entropy,tf.trainable_variables())) with tf.device('/cpu:0'): grad = average_gradients(grads) train_step = opt.apply_gradients(grad) yy=model_forward(x_dict['x0']) correct_prediction = tf.equal(tf.argmax(yy, 1), tf.argmax(y_dict['y0'], 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy') def main(): mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess: sess.run(tf.global_variables_initializer()) writer = tf.summary.FileWriter('C:\\tmp\\test\\', graph=tf.get_default_graph()) t1_1 = datetime.datetime.now() for step in range(0,10000): batch_x, batch_y = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5}) if (step % 200) == 0: print(step, sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1})) t2_1 = datetime.datetime.now() print("Computation time: " + str(t2_1-t1_1)) if __name__ == "__main__": main() 的门户网站。