From a8379345c70a27349d8341a54c77452d22a072f1 Mon Sep 17 00:00:00 2001 From: Crista Lopes Date: Tue, 31 Dec 2019 10:17:12 -0800 Subject: [PATCH] Clean up --- 38-rnn/tf-38.py | 21 ++------------------- 1 file changed, 2 insertions(+), 19 deletions(-) diff --git a/38-rnn/tf-38.py b/38-rnn/tf-38.py index f077601..2e9296e 100644 --- a/38-rnn/tf-38.py +++ b/38-rnn/tf-38.py @@ -1,9 +1,5 @@ from keras.models import Sequential -from keras.layers import Dense, LSTM, RepeatVector, TimeDistributed -from keras.losses import binary_crossentropy, categorical_crossentropy -from keras.optimizers import SGD -from keras. metrics import top_k_categorical_accuracy -from keras import backend as K +from keras.layers import Dense, LSTM import numpy as np import sys, os, string, random @@ -14,13 +10,9 @@ indices_char = dict((i, c) for i, c in enumerate(characters)) INPUT_VOCAB_SIZE = len(characters) BATCH_SIZE = 200 HIDDEN_SIZE = 100 -N_LAYERS = 1 TIME_STEPS = 3 def encode_one_hot(line): -# remain = len(line) % TIME_STEPS -# if remain != 0: -# line = line + ' ' * (TIME_STEPS-remain) line = " " + line x = np.zeros((len(line), INPUT_VOCAB_SIZE)) for i, c in enumerate(line): @@ -40,8 +32,6 @@ def prepare_for_rnn(x): ind = [np.array(np.arange(i, i+TIME_STEPS)) for i in range(x.shape[0] - TIME_STEPS + 1)] ind = np.array(ind, dtype=np.int32) x_rnn = x[ind] -# np.append(x_rnn, np.zeros((TIME_STEPS, INPUT_VOCAB_SIZE)), axis=0) -# np.append(x_rnn, np.zeros((TIME_STEPS, INPUT_VOCAB_SIZE)), axis=0) return x_rnn def input_generator(nsamples): @@ -63,8 +53,6 @@ def input_generator(nsamples): while True: input_data, expected = generate_line() - #print("Input :", input_data) - #print("Output:", expected) data_in = encode_one_hot(input_data) data_out = encode_one_hot(expected) yield prepare_for_rnn(data_in), data_out @@ -83,12 +71,7 @@ def train(model): def build_model(): model = Sequential() - r_layer = LSTM(HIDDEN_SIZE, input_shape=(None, INPUT_VOCAB_SIZE)) - model.add(r_layer) -# model.add(RepeatVector(TIME_STEPS)) -# for _ in range(N_LAYERS): -# model.add(LSTM(HIDDEN_SIZE, return_sequences=True)) -# model.add(TimeDistributed(Dense(INPUT_VOCAB_SIZE, activation='softmax'))) + model.add(LSTM(HIDDEN_SIZE, input_shape=(None, INPUT_VOCAB_SIZE))) model.add(Dense(INPUT_VOCAB_SIZE, activation='softmax')) return model