def test_loss(self): nnet, weightTerms = function_fit.inference(self.x, self.hidden1, self.hidden2) modError, modLoss = function_fit.loss(nnet, weightTerms, self.regLambda, self.y) init_op = tf.initialize_all_variables() with tf.Session() as sess: sess.run(init_op) print("loss=%r" % sess.run(modLoss, feed_dict={self.x:np.ones((3,1), dtype=np.float32), self.y:np.ones((3,1), dtype=np.float32)}))
def mainAfterWithGraph(functionData, mod): # Generate placeholders for the input and labels mod.x_input = tf.placeholder(tf.float32, shape=(None, 1)) mod.y_output = tf.placeholder(tf.float32, shape=(None, 1)) # Build a Graph that computes predictions from the inference model. mod = ffit.inference(mod, mod.x_input, FLAGS.LayerNodes, FLAGS.actfn, FLAGS.regNorm) # Add to the Graph the Ops for loss calculation. mod.modelError, mod.loss = ffit.loss(mod.nnetModel, mod.W_regterms, FLAGS.reg, mod.y_output) # Add to the Graph the Ops that calculate and apply gradients. mod = ffit.training(mod, mod.loss, FLAGS.learning_rate) # Build the summary operation based on the TF collection of Summaries. # mod.train_summary_op = tf.merge_all_summaries() # Create a saver for writing training checkpoints. # saver = tf.train.Saver() # Create a session for running Ops on the Graph. sess = tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=4)) # Run the Op to initialize the variables. init = tf.initialize_all_variables() sess.run(init) # Instantiate a SummaryWriter to output summaries and the Graph. # summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, # graph_def=sess.graph_def) h5out = tfh5.TensorFlowH5Writer(FLAGS.summaryfile) FLAGS.h5write(h5out.h5) functionData.h5write(h5out.h5) train_feed_dict = {mod.x_input: functionData.x_train, mod.y_output:functionData.y_train} test_feed_dict = {mod.x_input: functionData.x_test, mod.y_output:functionData.y_test} all_feed_dict = {mod.x_input: functionData.x_all, mod.y_output:functionData.y_all} # And then after everything is built, start the training loop. # saver.restore(sess, FLAGS.train_dir) mod.grad = None mod.gradMag = None for step in xrange(FLAGS.max_steps): trainStep(step, mod, sess, h5out, train_feed_dict, test_feed_dict, all_feed_dict)