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75 lines
2.3 KiB
75 lines
2.3 KiB
1 year ago
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import torch
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import os
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from config import *
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from utils import *
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import numpy as np
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import scipy.io as scio
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import tensorflow as tf
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import tensorflow.keras as keras
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from keras.models import Sequential
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from keras.layers import Dense,Dropout,Activation,Flatten
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from keras.layers import Conv2D,GlobalAveragePooling2D,MaxPooling2D,ZeroPadding2D,BatchNormalization
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from tensorflow.keras.optimizers import Adam
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import matplotlib.pyplot as plt
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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np.set_printoptions(threshold=np.inf)
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train_data, train_ans, test_data, test_ans = load_data()
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# train_data = train_data.reshape(800,10,32,1)
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train_data = tf.expand_dims(train_data, axis=-1)
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print(train_data.shape)
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model=Sequential()
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# model.add(Dense(20))
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# model.add(Activation("relu"))
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# model.add(Dense(1))
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# model.add(Activation("relu"))
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model.add(Conv2D(filters=1,kernel_size=(10,32),
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strides=(1,1),padding='valid',
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input_shape=(10,32,1),
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activation='relu'))
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# train the model using Adam
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print("Training network")
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model.compile(loss='mean_squared_logarithmic_error',
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optimizer='adam',
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metrics=['accuracy'])
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H = model.fit(train_data,train_ans,validation_data=(test_data,test_ans),
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epochs=Epochs,batch_size=BatchSize)
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model.save_weights("model.h5")
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print("Save model to disk")
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# evaluate the network
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print("Evaluating network")
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loss, accuracy = model.evaluate(train_data,train_ans)
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print("\nLoss: %.2f, Accuracy: %.2f%%" % (loss, accuracy*100))
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# test
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print("Test network")
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pred = model.predict(test_data,batch_size=BatchSize)
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pred = pred.reshape((200,))
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print(np.abs(pred-test_ans).shape)
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print(test_ans.shape)
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acc = np.mean(np.abs(pred-test_ans) < 1000)
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print(pred.shape)
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print('Prediction Accuracy: %.2f%%' % (acc*100))
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# # plot the training loss and accuracy
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# plt.style.use("ggplot")
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# plt.figure()
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# plt.plot(np.arange(Epochs), H.history["loss"], label="train_loss")
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# plt.plot(np.arange(Epochs), H.history["val_loss"], label="val_loss")
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# plt.plot(np.arange(Epochs), H.history["accuracy"], label="train_acc")
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# plt.plot(np.arange(Epochs), H.history["val_accuracy"], label="val_acc")
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# plt.title("Training Loss and Accuracy")
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# plt.xlabel("Epoch #")
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# plt.ylabel("Loss/Accuracy")
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# plt.legend()
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# plt.savefig("LOSS.jpg")
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print("Model summary")
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model.summary()
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