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main.py
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main.py
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import os
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=gpu,floatX=float32"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ["CUDA_VISIBLE_DEVICES"]="2,1,0"
from sklearn.metrics import accuracy_score
import numpy as np
import itertools
import matplotlib.pyplot as plt
import gc
import Data_load
import BuildModel
from keras.callbacks import ModelCheckpoint
np.random.seed(7)
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
if __name__ == "__main__":
batch_size = 128
n_epochs = 10
sparse_categorical=0
text=1
np.set_printoptions(threshold=np.inf)
np.random.seed(7)
if text==1:
y_proba = []
model_DNN = []
model_RNN = []
model_CNN = []
History = []
score = []
X_train, y_train,X_test, y_test, number_of_classes = Data_load.Load_data()
print("DNN ")
filepath = "weights_DNN.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
model_DNN, model_tmp = BuildModel.buildModel_DNN_Tex(X_train.shape[1],number_of_classes,sparse_categorical)
h = model_DNN.fit(X_train, y_train,
validation_data=(X_test, y_test),
epochs=n_epochs,
batch_size=batch_size,
callbacks=callbacks_list,
verbose=2)
History.append(h)
model_tmp.load_weights("weights_DNN.hdf5")
if sparse_categorical==0:
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
y_pr = model_tmp.predict_classes(X_test, batch_size=batch_size)
y_proba.append(np.array(y_pr))
score.append(accuracy_score(y_test, y_pr))
else:
model_tmp.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
y_pr = model_tmp.predict(X_test, batch_size=batch_size)
y_pr = np.argmax(y_pr,axis=1)
y_proba.append(np.array(y_pr))
y_test_temp = np.argmax(y_test,axis=1)
score.append(accuracy_score(y_test_temp, y_pr))
del model_tmp
del model_DNN
gc.collect()
print(score)