382 lines
13 KiB
Python
Executable File
382 lines
13 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import warnings
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warnings.filterwarnings('ignore')
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import numpy as np
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import getopt
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import sys
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import os
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import math
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import time
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import argparse
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from visdom import Visdom
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sys.path.insert(0, os.path.join('..', '..'))
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import torch as T
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from torch.autograd import Variable as var
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import torch.nn.functional as F
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import torch.optim as optim
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from torch.nn.utils import clip_grad_norm_
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from dnc.dnc import DNC
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from dnc.sdnc import SDNC
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from dnc.sam import SAM
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from dnc.util import *
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parser = argparse.ArgumentParser(description='PyTorch Differentiable Neural Computer')
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parser.add_argument('-input_size', type=int, default=6, help='dimension of input feature')
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parser.add_argument('-rnn_type', type=str, default='lstm', help='type of recurrent cells to use for the controller')
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parser.add_argument('-nhid', type=int, default=64, help='number of hidden units of the inner nn')
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parser.add_argument('-dropout', type=float, default=0, help='controller dropout')
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parser.add_argument('-memory_type', type=str, default='dnc', help='dense or sparse memory: dnc | sdnc | sam')
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parser.add_argument('-nlayer', type=int, default=1, help='number of layers')
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parser.add_argument('-nhlayer', type=int, default=2, help='number of hidden layers')
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parser.add_argument('-lr', type=float, default=1e-4, help='initial learning rate')
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parser.add_argument('-optim', type=str, default='adam', help='learning rule, supports adam|rmsprop')
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parser.add_argument('-clip', type=float, default=50, help='gradient clipping')
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parser.add_argument('-batch_size', type=int, default=100, metavar='N', help='batch size')
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parser.add_argument('-mem_size', type=int, default=20, help='memory dimension')
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parser.add_argument('-mem_slot', type=int, default=16, help='number of memory slots')
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parser.add_argument('-read_heads', type=int, default=4, help='number of read heads')
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parser.add_argument('-sparse_reads', type=int, default=10, help='number of sparse reads per read head')
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parser.add_argument('-temporal_reads', type=int, default=2, help='number of temporal reads')
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parser.add_argument('-sequence_max_length', type=int, default=4, metavar='N', help='sequence_max_length')
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parser.add_argument('-curriculum_increment', type=int, default=0, metavar='N', help='sequence_max_length incrementor per 1K iterations')
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parser.add_argument('-curriculum_freq', type=int, default=1000, metavar='N', help='sequence_max_length incrementor per 1K iterations')
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parser.add_argument('-cuda', type=int, default=-1, help='Cuda GPU ID, -1 for CPU')
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parser.add_argument('-iterations', type=int, default=100000, metavar='N', help='total number of iteration')
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parser.add_argument('-summarize_freq', type=int, default=100, metavar='N', help='summarize frequency')
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parser.add_argument('-check_freq', type=int, default=100, metavar='N', help='check point frequency')
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parser.add_argument('-visdom', action='store_true', help='plot memory content on visdom per -summarize_freq steps')
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args = parser.parse_args()
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print(args)
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viz = Visdom()
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# assert viz.check_connection()
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if args.cuda != -1:
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print('Using CUDA.')
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T.manual_seed(1111)
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else:
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print('Using CPU.')
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def llprint(message):
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sys.stdout.write(message)
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sys.stdout.flush()
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def generate_data(batch_size, length, size, cuda=-1):
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input_data = np.zeros((batch_size, 2 * length + 1, size), dtype=np.float32)
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target_output = np.zeros((batch_size, 2 * length + 1, size), dtype=np.float32)
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sequence = np.random.binomial(1, 0.5, (batch_size, length, size - 1))
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input_data[:, :length, :size - 1] = sequence
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input_data[:, length, -1] = 1 # the end symbol
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target_output[:, length + 1:, :size - 1] = sequence
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input_data = T.from_numpy(input_data)
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target_output = T.from_numpy(target_output)
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if cuda != -1:
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input_data = input_data.cuda()
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target_output = target_output.cuda()
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return var(input_data), var(target_output)
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def criterion(predictions, targets):
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return T.mean(
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-1 * F.logsigmoid(predictions) * (targets) - T.log(1 - F.sigmoid(predictions) + 1e-9) * (1 - targets)
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)
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if __name__ == '__main__':
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dirname = os.path.dirname(__file__)
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ckpts_dir = os.path.join(dirname, 'checkpoints')
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if not os.path.isdir(ckpts_dir):
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os.mkdir(ckpts_dir)
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batch_size = args.batch_size
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sequence_max_length = args.sequence_max_length
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iterations = args.iterations
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summarize_freq = args.summarize_freq
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check_freq = args.check_freq
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# input_size = output_size = args.input_size
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mem_slot = args.mem_slot
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mem_size = args.mem_size
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read_heads = args.read_heads
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if args.memory_type == 'dnc':
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rnn = DNC(
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input_size=args.input_size,
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hidden_size=args.nhid,
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rnn_type=args.rnn_type,
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num_layers=args.nlayer,
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num_hidden_layers=args.nhlayer,
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dropout=args.dropout,
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nr_cells=mem_slot,
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cell_size=mem_size,
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read_heads=read_heads,
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gpu_id=args.cuda,
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debug=args.visdom,
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batch_first=True,
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independent_linears=True
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)
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elif args.memory_type == 'sdnc':
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rnn = SDNC(
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input_size=args.input_size,
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hidden_size=args.nhid,
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rnn_type=args.rnn_type,
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num_layers=args.nlayer,
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num_hidden_layers=args.nhlayer,
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dropout=args.dropout,
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nr_cells=mem_slot,
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cell_size=mem_size,
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sparse_reads=args.sparse_reads,
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temporal_reads=args.temporal_reads,
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read_heads=args.read_heads,
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gpu_id=args.cuda,
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debug=args.visdom,
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batch_first=True,
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independent_linears=False
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)
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elif args.memory_type == 'sam':
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rnn = SAM(
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input_size=args.input_size,
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hidden_size=args.nhid,
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rnn_type=args.rnn_type,
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num_layers=args.nlayer,
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num_hidden_layers=args.nhlayer,
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dropout=args.dropout,
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nr_cells=mem_slot,
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cell_size=mem_size,
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sparse_reads=args.sparse_reads,
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read_heads=args.read_heads,
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gpu_id=args.cuda,
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debug=args.visdom,
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batch_first=True,
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independent_linears=False
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)
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else:
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raise Exception('Not recognized type of memory')
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print(rnn)
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# register_nan_checks(rnn)
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if args.cuda != -1:
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rnn = rnn.cuda(args.cuda)
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last_save_losses = []
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if args.optim == 'adam':
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optimizer = optim.Adam(rnn.parameters(), lr=args.lr, eps=1e-9, betas=[0.9, 0.98]) # 0.0001
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elif args.optim == 'adamax':
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optimizer = optim.Adamax(rnn.parameters(), lr=args.lr, eps=1e-9, betas=[0.9, 0.98]) # 0.0001
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elif args.optim == 'rmsprop':
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optimizer = optim.RMSprop(rnn.parameters(), lr=args.lr, momentum=0.9, eps=1e-10) # 0.0001
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elif args.optim == 'sgd':
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optimizer = optim.SGD(rnn.parameters(), lr=args.lr) # 0.01
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elif args.optim == 'adagrad':
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optimizer = optim.Adagrad(rnn.parameters(), lr=args.lr)
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elif args.optim == 'adadelta':
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optimizer = optim.Adadelta(rnn.parameters(), lr=args.lr)
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(chx, mhx, rv) = (None, None, None)
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for epoch in range(iterations + 1):
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llprint("\rIteration {ep}/{tot}".format(ep=epoch, tot=iterations))
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optimizer.zero_grad()
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random_length = np.random.randint(1, sequence_max_length + 1)
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input_data, target_output = generate_data(batch_size, random_length, args.input_size, args.cuda)
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if rnn.debug:
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output, (chx, mhx, rv), v = rnn(input_data, (None, mhx, None), reset_experience=True, pass_through_memory=True)
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else:
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output, (chx, mhx, rv) = rnn(input_data, (None, mhx, None), reset_experience=True, pass_through_memory=True)
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loss = criterion((output), target_output)
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loss.backward()
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T.nn.utils.clip_grad_norm_(rnn.parameters(), args.clip)
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optimizer.step()
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loss_value = loss.item()
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summarize = (epoch % summarize_freq == 0)
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take_checkpoint = (epoch != 0) and (epoch % check_freq == 0)
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increment_curriculum = (epoch != 0) and (epoch % args.curriculum_freq == 0)
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# detach memory from graph
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mhx = { k : (v.detach() if isinstance(v, var) else v) for k, v in mhx.items() }
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last_save_losses.append(loss_value)
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if summarize:
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loss = np.mean(last_save_losses)
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# print(input_data)
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# print("1111111111111111111111111111111111111111111111")
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# print(target_output)
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# print('2222222222222222222222222222222222222222222222')
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# print(F.relu6(output))
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llprint("\n\tAvg. Logistic Loss: %.4f\n" % (loss))
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if np.isnan(loss):
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raise Exception('nan Loss')
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if summarize and rnn.debug:
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loss = np.mean(last_save_losses)
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# print(input_data)
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# print("1111111111111111111111111111111111111111111111")
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# print(target_output)
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# print('2222222222222222222222222222222222222222222222')
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# print(F.relu6(output))
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last_save_losses = []
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if args.memory_type == 'dnc':
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viz.heatmap(
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v['memory'],
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opts=dict(
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xtickstep=10,
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ytickstep=2,
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title='Memory, t: ' + str(epoch) + ', loss: ' + str(loss),
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ylabel='layer * time',
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xlabel='mem_slot * mem_size'
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)
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)
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if args.memory_type == 'dnc':
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viz.heatmap(
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v['link_matrix'][-1].reshape(args.mem_slot, args.mem_slot),
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opts=dict(
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xtickstep=10,
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ytickstep=2,
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title='Link Matrix, t: ' + str(epoch) + ', loss: ' + str(loss),
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ylabel='mem_slot',
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xlabel='mem_slot'
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)
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)
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elif args.memory_type == 'sdnc':
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viz.heatmap(
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v['link_matrix'][-1].reshape(args.mem_slot, -1),
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opts=dict(
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xtickstep=10,
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ytickstep=2,
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title='Link Matrix, t: ' + str(epoch) + ', loss: ' + str(loss),
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ylabel='mem_slot',
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xlabel='mem_slot'
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)
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)
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viz.heatmap(
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v['rev_link_matrix'][-1].reshape(args.mem_slot, -1),
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opts=dict(
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xtickstep=10,
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ytickstep=2,
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title='Reverse Link Matrix, t: ' + str(epoch) + ', loss: ' + str(loss),
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ylabel='mem_slot',
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xlabel='mem_slot'
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)
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)
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elif args.memory_type == 'sdnc' or args.memory_type == 'dnc':
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viz.heatmap(
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v['precedence'],
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opts=dict(
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xtickstep=10,
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ytickstep=2,
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title='Precedence, t: ' + str(epoch) + ', loss: ' + str(loss),
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ylabel='layer * time',
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xlabel='mem_slot'
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)
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)
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if args.memory_type == 'sdnc':
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viz.heatmap(
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v['read_positions'],
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opts=dict(
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xtickstep=10,
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ytickstep=2,
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title='Read Positions, t: ' + str(epoch) + ', loss: ' + str(loss),
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ylabel='layer * time',
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xlabel='mem_slot'
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)
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)
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viz.heatmap(
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v['read_weights'],
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opts=dict(
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xtickstep=10,
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ytickstep=2,
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title='Read Weights, t: ' + str(epoch) + ', loss: ' + str(loss),
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ylabel='layer * time',
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xlabel='nr_read_heads * mem_slot'
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)
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)
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viz.heatmap(
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v['write_weights'],
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opts=dict(
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xtickstep=10,
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ytickstep=2,
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title='Write Weights, t: ' + str(epoch) + ', loss: ' + str(loss),
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ylabel='layer * time',
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xlabel='mem_slot'
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)
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)
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viz.heatmap(
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v['usage_vector'] if args.memory_type == 'dnc' else v['usage'],
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opts=dict(
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xtickstep=10,
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ytickstep=2,
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title='Usage Vector, t: ' + str(epoch) + ', loss: ' + str(loss),
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ylabel='layer * time',
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xlabel='mem_slot'
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)
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)
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if increment_curriculum:
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sequence_max_length = sequence_max_length + args.curriculum_increment
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print("Increasing max length to " + str(sequence_max_length))
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if take_checkpoint:
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llprint("\nSaving Checkpoint ... "),
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check_ptr = os.path.join(ckpts_dir, 'step_{}.pth'.format(epoch))
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cur_weights = rnn.state_dict()
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T.save(cur_weights, check_ptr)
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llprint("Done!\n")
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for i in range(int((iterations + 1) / 10)):
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llprint("\nIteration %d/%d" % (i, iterations))
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# We test now the learned generalization using sequence_max_length examples
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random_length = np.random.randint(2, sequence_max_length * 10 + 1)
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input_data, target_output, loss_weights = generate_data(random_length, input_size)
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if rnn.debug:
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output, (chx, mhx, rv), v = rnn(input_data, (None, mhx, None), reset_experience=True, pass_through_memory=True)
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else:
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output, (chx, mhx, rv) = rnn(input_data, (None, mhx, None), reset_experience=True, pass_through_memory=True)
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output = output[:, -1, :].sum().data.cpu().numpy()[0]
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target_output = target_output.sum().data.cpu().numpy()
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try:
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print("\nReal value: ", ' = ' + str(int(target_output[0])))
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print("Predicted: ", ' = ' + str(int(output // 1)) + " [" + str(output) + "]")
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except Exception as e:
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pass
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