for k in range(folds): print("Fold: {0}".format(k)) trainingData = data[0][indices[k][0]] trainingLabels = data[1][indices[k][0]] testingData = data[0][indices[k][1]] testingLabels = data[1][indices[k][1]] with tf.Session() as sess: sess.run(tf.global_variables_initializer()) # Training for i in range(epochs): # Batches for j in range(0, trainingData.shape[0], b_size): x_raw = trainingData[j:j + b_size] y_raw = hotvector(trainingLabels[j:j + b_size], classes) [la, c] = sess.run([optimizer, cost], feed_dict={ x: x_raw, y: y_raw, phase: True }) #saver.save(sess, 'tmp/my-weights') #g = sess.graph #gdef = g.as_graph_def() #tf.train.write_graph(gdef,"tmp","graph.pb",False) # Testing c = 0
print(counter,len(allConfgs)) (x,y),optimizer,cost,eval_pred,saver = getModel(conf) b_size = conf[-1] with tf.Session() as sess: sess.run(tf.global_variables_initializer()) merged = tf.summary.merge_all() writer = tf.summary.FileWriter("logs/"+conf2str(conf),sess.graph) step = 0 # Training for i in range(epochs): # Batches for j in range(0,data["train"][1].shape[0],b_size): x_raw = data["train"][0][j:j+b_size] y_raw = hotvector(data["train"][1][j:j+b_size],classes) [la,c,summary]=sess.run([optimizer,cost,merged], feed_dict={x: x_raw, y: y_raw}) writer.add_summary(summary,step) step+=1 writer.close() #saver.save(sess, 'tmp/my-weights') #g = sess.graph #gdef = g.as_graph_def() #tf.train.write_graph(gdef,"tmp","graph.pb",False) # Testing c=0;g=0 goodones=[]