checkEnv.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

torch.manual_seed(1)

lstm = nn.LSTM(3, 6)  # Input dim is 3, output dim is 3
inputs = [torch.randn(1, 3) for _ in range(5)]  # make a sequence of length 5

# initialize the hidden state.
hidden = (torch.randn(1, 1, 6),
          torch.randn(1, 1, 6))
for i in inputs:
    # Step through the sequence one element at a time.
    # after each step, hidden contains the hidden state.
    out, hidden = lstm(i.view(1, 1, -1), hidden)

# print(out)
# print(hidden)

inputs = torch.cat(inputs).view(len(inputs), 1, -1)
hidden = (torch.randn(1, 1, 6), torch.randn(1, 1, 6))  # clean out hidden state
out, hidden = lstm(inputs, hidden)
# print(out)
# print(hidden)
training_data = [
    ("The dog ate the apple".split(), ["DET", "NN", "V", "DET", "NN"]),
    ("Everybody read that book".split(), ["NN", "V", "DET", "NN"])
]

o=0
word_to_ix = {}
for sent,tags in training_data:
    for word in sent:
        # print(word)
        if word not in word_to_ix:
            word_to_ix[word] = len(word_to_ix)
# print(word_to_ix)
#
#
# print(training_data[0][0])