clf = initializeClassifier('ANN', x_train, ANNparams)
history = clf.fit(x_train,
y_train_enc,
epochs=ANNparams[5],
verbose=1,
validation_data=(x_test, y_test_enc))
#history1 = clf.fit(x_train, y_train_enc, epochs=5000, verbose = 1, validation_data = (x_test, y_test_enc))
plt.plot(history.history['val_loss'], 'k', label='Test loss')
plt.plot(history.history['val_acc'], 'k:', label='Test accuracy')
plt.xlabel('number of epochs')
legend = plt.legend(loc='upper center', fontsize='x-large')
plt.ylim(0, 1.5)
plt.show()
y_pred = clf.predict_classes(x_test)
if alg == 'CNN':
x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], 1))
x_test = x_test.reshape((x_test.shape[0], x_test.shape[1], 1))
clf = initializeFCN(x_train, CNNparams)
history = clf.fit(x_train,
y_train_enc,
epochs=CNNparams[7],
verbose=0,
validation_data=(x_test, y_test_enc))
plt.plot(history.history['val_loss'], 'k', label='Test loss')
plt.plot(history.history['val_acc'], 'k:', label='Test accuracy')
verbose=0,
validation_data=(x_val, y_val_enc))
bestValAcc = np.max(history.history['val_acc'])
bestValLoss = np.min(history.history['val_loss'])
bestValAccEpoch = np.argmax(history.history['val_acc'])
bestValLossEpoch = np.argmin(history.history['val_loss'])
# plt.plot(history.history['val_loss'], 'k', label='Validation loss')
# plt.plot(history.history['val_acc'], 'k:', label='Validation accuracy')
# plt.xlabel('number of epochs')
# legend = plt.legend(loc='upper center', fontsize='x-large')
# plt.ylim(0,1.5)
# plt.show()
y_pred = clf.predict_classes(x_val)
if alg == 'CNN':
x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], 1))
x_val = x_val.reshape((x_val.shape[0], x_val.shape[1], 1))
clf = initializeFCN(x_train, params)
history = clf.fit(x_train,
y_train_enc,
epochs=2000,
verbose=0,
validation_data=(x_val, y_val_enc))
bestValAcc = np.max(history.history['val_acc'])
bestValLoss = np.min(history.history['val_loss'])
bestValAccEpoch = np.argmax(history.history['val_acc'])
labels = np.array(labels)
print("[INFO] features matrix: {:.2f}MB".format(features.nbytes /
(1024 * 1000.0)))
# partition the data into training and testing splits, using 80%
# of the data for training and the remaining 20% for testing
(trainFeat, testFeat, trainLabels,
testLabels) = train_test_split(features,
labels,
test_size=0.2,
random_state=42)
# train and evaluate a k-NN classifer on the histogram
# representations
print("[INFO] evaluating histogram accuracy...")
model = KNeighborsClassifier(n_neighbors=20, n_jobs=-1)
model.fit(trainFeat, trainLabels)
acc = model.score(testFeat, testLabels)
score_feat = model.predict_classes(testFeat)
print("Detailed classification report")
print(classification_report(testLabels, score_feat))
