!D:/workplace/python-*- coding: utf-8 -*-@File : heart.py@Author: WangYe@Date : 2019/4/23@Software: PyCharm!D:/workplace/python-*- coding: utf-8 -*-@File : lstm_test.py@Author: WangYe@Date : 2018/8/1@Software: PyCharm

import numpyfrom keras.models import Sequential from keras.layers import Dense, LSTM, Activation from keras.activations import relu, tanh from keras.utils import np_utils

fix random seed for reproducibility

numpy.random.seed(7)

define the raw dataset

alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"

create mapping of characters to integers (0-25) and the reverse

char_to_int = dict((c, i) for i, c in enumerate(alphabet))int_to_char = dict((i, c) for i, c in enumerate(alphabet))

prepare the dataset of input to output pairs encoded as integers

seq_length = 3dataX = [] dataY = [] for i in range(0, len(alphabet) - seq_length, 1): seq_in = alphabet[i:i + seq_length] seq_out = alphabet[i + seq_length] dataX.append([char_to_int[char] for char in seq_in]) dataY.append(char_to_int[seq_out]) print(seq_in, '->', seq_out)

reshape X to be [samples, time steps, features]print(alphabet)

X = numpy.reshape(dataX, (len(dataX), 1, seq_length))

将X转化为24个元素(abc等换为123数字)，每个元素的长度为1在这一个元素中，有存着3个元素，所以为[[[22 23 24]]]的矩阵print(dataY)normalize

X = X / float(len(alphabet))#X转换为0到1print(X)

one hot encode the output variable

y = np_utils.to_categorical(dataY)#转换成一个标签的矩阵print(y)

链接：print(X)print(X.shape[0])#23print(X.shape[1])#1print(X.shape[2])#3print(y.shape[0])#23print(y.shape[1])#26create and fit the model

model = Sequential()model.add(LSTM(units=32, input_shape=(X.shape[1], X.shape[2]))) # units model.add(Activation(relu)) model.add(Dense(y.shape[1], activation='softmax')) #全链接层 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(X, y, nb_epoch=500, batch_size=1, verbose=2)

summarize performance of the model

scores = model.evaluate(X, y, verbose=0)print("Model Accuracy: %.2f%%" % (scores[1]*100))

demonstrate some model predictions

for pattern in dataX: x = numpy.reshape(pattern, (1, 1, len(pattern))) x = x / float(len(alphabet)) prediction = model.predict(x, verbose=0) index = numpy.argmax(prediction) result = int_to_char[index] seq_in = [int_to_char[value] for value in pattern] # print(seq_in, "->", result)

关键代码

import timesentence = "WY love LQP" for char in sentence.split(): allChar = [] for y in range(12, -12, -1): lst = [] lst_con = '' for x in range(-30, 30): formula = ((x*0.05)**2+(y*0.1) 2-1)3-(x*0.05)**2*(y*0.1)**3 if formula <= 0: lst_con += char[(x) % len(char)] else: lst_con += ' ' lst.append(lst_con) allChar += lst print('\n'.join(allChar)) time.sleep(1

单身狗的可以试一下?