rnn_playTuple.py

# encoding: UTF-8
# Copyright 2017 Google.com
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import tensorflow as tf
import numpy as np
import my_txtutils

target = open("outputs.txt", 'w')

# these must match what was saved !
ALPHASIZE = my_txtutils.ALPHASIZE
NLAYERS = 3
INTERNALSIZE = 512
BATCHSIZE = 100
# use topn=10 for all but the last one which works with topn=2 for Shakespeare and topn=3 for Python
author = "twitterTuple/rnn_train_1498228113-45000000"
ncnt = 0
with tf.Session() as sess:
    new_saver = tf.train.import_meta_graph('twitterTuple/rnn_train_1498228113-45000000.meta')
    new_saver.restore(sess, author)
    x = my_txtutils.convert_from_alphabet(ord("L"))
    x = np.array([[x]])  # shape [BATCHSIZE, SEQLEN] with BATCHSIZE=1 and SEQLEN=1
    print("x shape is ", x)
    # initial values
    y = x
    h = np.zeros([NLAYERS, BATCHSIZE ,INTERNALSIZE], dtype=np.float32)  # [ BATCHSIZE, INTERNALSIZE * NLAYERS]
    for i in range(1000000000):
        # yo, h = sess.run(['Yo:0', 'H:0'], feed_dict={'X:0': y, 'pkeep:0': 1., 'Hin:0': h, 'batchsize:0': 1})
        # feed_dict = {X: x, Y_: y_, Hin: istate, lr: learning_rate, pkeep: dropout_pkeep, batchsize: BATCHSIZE}
        # _, y, ostate = sess.run([train_step, Y, H], feed_dict=feed_dict)
        print("y shape ", y.shape)
        print("h shape ", h.shape)
        yo, h = sess.run(['Yo:0', 'H:0'], feed_dict={'X:0': y, 'H:0': h, 'batchsize:0': 1})

        # # If sampling is be done from the topn most likely characters, the generated text
        # # is more credible and more "english". If topn is not set, it defaults to the full
        # # distribution (ALPHASIZE)
        #
        # # Recommended: topn = 10 for intermediate checkpoints, topn=2 or 3 for fully trained checkpoints
        #
        # c = my_txtutils.sample_from_probabilities(yo, topn=2)
        # y = np.array([[c]])  # shape [BATCHSIZE, SEQLEN] with BATCHSIZE=1 and SEQLEN=1
        # c = chr(my_txtutils.convert_to_alphabet(c))
        # print(c, end="")
        # target.write(c)
        #
        # if c == '\n':
        #     ncnt = 0
        # else:
        #     ncnt += 1
        # if ncnt == 100:
        #     print("")
        #     ncnt = 0

target.close()