{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##### 교재페이지 (328-329)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "8) RNN with long sequences" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "idx2char = ['d','a','t','a','e','d','u'] # d=0, a=1, t=2, e=3, d=4, u=5\n", "x_data = [[[0, 1, 2, 1, 3, 4]]]# dataed\n", "x_one_hot = [[[1,0,0,0,0,0], #d\n", " [0,1,0,0,0,0], #a\n", " [0,0,1,0,0,0], #t\n", " [0,0,0,1,0,0], #e\n", " [0,0,0,0,1,0], #d\n", " [0,0,0,0,0,1]]] #u\n", "\n", "y_data = [[1, 2, 1, 3, 4, 5]] # ataedu" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", "For more information, please see:\n", " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", " * https://github.com/tensorflow/addons\n", "If you depend on functionality not listed there, please file an issue.\n", "\n", "WARNING:tensorflow:From :27: BasicLSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.\n", "WARNING:tensorflow:From :30: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n", "WARNING:tensorflow:From C:\\Users\\jsdata00010\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\tensor_array_ops.py:162: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Colocations handled automatically by placer.\n", "0 loss: 1.9187785 Prediction: dadauuuuauu\n", "1 loss: 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dataedu day\n", "1936 loss: 0.81818366 Prediction: dataedu day\n", "1937 loss: 0.8181834 Prediction: dataedu day\n", "1938 loss: 0.8181831 Prediction: dataedu day\n", "1939 loss: 0.8181828 Prediction: dataedu day\n", "1940 loss: 0.8181825 Prediction: dataedu day\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1941 loss: 0.8181823 Prediction: dataedu day\n", "1942 loss: 0.818182 Prediction: dataedu day\n", "1943 loss: 0.81818175 Prediction: dataedu day\n", "1944 loss: 0.8181815 Prediction: dataedu day\n", "1945 loss: 0.81818116 Prediction: dataedu day\n", "1946 loss: 0.81818086 Prediction: dataedu day\n", "1947 loss: 0.8181806 Prediction: dataedu day\n", "1948 loss: 0.81818044 Prediction: dataedu day\n", "1949 loss: 0.8181801 Prediction: dataedu day\n", "1950 loss: 0.81817985 Prediction: dataedu day\n", "1951 loss: 0.8181796 Prediction: dataedu day\n", "1952 loss: 0.8181793 Prediction: dataedu day\n", "1953 loss: 0.8181791 Prediction: dataedu day\n", "1954 loss: 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"1974 loss: 0.8181735 Prediction: dataedu day\n", "1975 loss: 0.81817335 Prediction: dataedu day\n", "1976 loss: 0.818173 Prediction: dataedu day\n", "1977 loss: 0.81817275 Prediction: dataedu day\n", "1978 loss: 0.81817245 Prediction: dataedu day\n", "1979 loss: 0.8181723 Prediction: dataedu day\n", "1980 loss: 0.818172 Prediction: dataedu day\n", "1981 loss: 0.8181718 Prediction: dataedu day\n", "1982 loss: 0.81817144 Prediction: dataedu day\n", "1983 loss: 0.81817126 Prediction: dataedu day\n", "1984 loss: 0.818171 Prediction: dataedu day\n", "1985 loss: 0.8181707 Prediction: dataedu day\n", "1986 loss: 0.8181705 Prediction: dataedu day\n", "1987 loss: 0.81817025 Prediction: dataedu day\n", "1988 loss: 0.8181699 Prediction: dataedu day\n", "1989 loss: 0.8181697 Prediction: dataedu day\n", "1990 loss: 0.8181694 Prediction: dataedu day\n", "1991 loss: 0.8181692 Prediction: dataedu day\n", "1992 loss: 0.81816894 Prediction: dataedu day\n", "1993 loss: 0.81816864 Prediction: dataedu day\n", "1994 loss: 0.81816846 Prediction: dataedu day\n", "1995 loss: 0.81816816 Prediction: dataedu day\n", "1996 loss: 0.818168 Prediction: dataedu day\n", "1997 loss: 0.8181676 Prediction: dataedu day\n", "1998 loss: 0.81816745 Prediction: dataedu day\n", "1999 loss: 0.8181671 Prediction: dataedu day\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "tf.set_random_seed(777) # reproducibility\n", "\n", "sample = \" dataedu day\"\n", "idx2char = list(set(sample)) # index -> char\n", "char2idx = {c : i for i, c in enumerate(idx2char)} # character 를 주면 index로 만든다\n", "\n", "dic_size = len(char2idx) # RNN input size (one hot size)\n", "rnn_hidden_size = len(char2idx) # RNN output size\n", "num_classes = len(char2idx) # final output size (RNN or softmax, etc.)\n", "batch_size = 1 # one sample data, one batch\n", "sequence_length = len(sample)-1 # number of lstm rollings (unit #)\n", "learning_rate = 0.1\n", "\n", "\n", "# Teach matchup tensorflow class matchup tensorflow clas -> atchup tensorflow class\n", "sample_idx = [char2idx[c] for c in sample]\n", "x_data = [sample_idx[:-1]] # X data sample (0 ~ n-1)\n", "y_data = [sample_idx[1:]] # Y label sample (1 ~ n)\n", "\n", "X = tf.placeholder(tf.int32, [None, sequence_length]) # X data\n", "Y = tf.placeholder(tf.int32, [None, sequence_length]) # Y label\n", "\n", "X_one_hot = tf.one_hot(X, num_classes)\n", "\n", "cell = tf.contrib.rnn.BasicLSTMCell(num_units=rnn_hidden_size, state_is_tuple=True)\n", "initial_state = cell.zero_state(batch_size, tf.float32)\n", "outputs, _states = tf.nn.dynamic_rnn(\n", " cell, X_one_hot, initial_state=initial_state, dtype=tf.float32)\n", "weights = tf.ones([batch_size, sequence_length])\n", "sequence_loss = tf.contrib.seq2seq.sequence_loss(logits=outputs, targets=Y,weights=weights)\n", "loss = tf.reduce_mean(sequence_loss)\n", "train = tf.train.AdamOptimizer(learning_rate=0.1).minimize(loss)\n", "prediction = tf.argmax(outputs, axis=2)\n", "\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for i in range(2000):\n", " l, _ = sess.run([loss, train], feed_dict={X: x_data, Y: y_data})\n", " result = sess.run(prediction, feed_dict={X: x_data})\n", " # print char using dic\n", " result_str = [idx2char[c] for c in np.squeeze(result)]\n", " print(i, \"loss:\", l, \"Prediction:\", ''.join(result_str))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }