def epoch(eta=0.04,
penalty=0.4,
epochs=200,
mini_batch_size=100,
t0=5,
t1=50,
create_conf=False):
layer1 = DenseLayer(features, 100, sigmoid())
#layer2 = DenseLayer(100, 50, sigmoid())
#layer3 = DenseLayer(100, 50, sigmoid())
layer4 = DenseLayer(100, 10, softmax())
layers = [layer1, layer4]
network = NN(layers)
cost_array = np.zeros((epochs, 2))
def learning_schedule(t):
return 0.04 #t0/(t+t1)
for i in range(epochs):
random.shuffle(batch)
X_train_shuffle = X_train[batch]
one_hot_shuffle = one_hot[batch]
Y_train_shuffle = Y_train[batch]
#eta = learning_schedule(i)
network.SGD(ce, 100, X_train_shuffle, one_hot_shuffle, eta, penalty)
Y_pred = np.argmax(network.feedforward(X_test), axis=1)
Y_pred_train = np.argmax(network.feedforward(X_train_shuffle), axis=1)
cost_array[i, 0] = accuracy()(Y_test.ravel(), Y_pred)
cost_array[i, 1] = accuracy()(Y_train_shuffle.ravel(), Y_pred_train)
print("accuracy on train data = %.3f" % cost_array[-1, 1])
print("accuracy on test data = %.3f" % cost_array[-1, 0])
if create_conf == True:
#creating confusion matrix
numbers = np.arange(0, 10)
conf_matrix = confusion_matrix(Y_pred, Y_test, normalize="true")
heatmap = sb.heatmap(conf_matrix,
cmap="viridis",
xticklabels=["%d" % i for i in numbers],
yticklabels=["%d" % i for i in numbers],
cbar_kws={'label': 'Accuracy'},
fmt=".2",
edgecolor="none",
annot=True)
heatmap.set_xlabel("pred")
heatmap.set_ylabel("true")
heatmap.set_title(r"FFNN prediction accuracy with $\lambda$ = {:.1e} $\eta$ = {:.1e}"\
.format(penalty, eta))
fig = heatmap.get_figure()
fig.savefig("../figures/MNIST_confusion_net.pdf",
bbox_inches='tight',
pad_inches=0.1,
dpi=1200)
plt.show()
return cost_array[-1]
trainingSet = []
### each training iteration trains the network on 3 chorales at a time
for j in range(3):
randomChoraleNo = i * 3 + j
choraleArray = MusicHandler.getChoraleArray(
i, 'jsbach_chorals_harmony.data')
trainingSet.extend(getTrainingSet(choraleArray))
print("Length of training set: " + str(len(trainingSet)))
### crop training data to a multiple of batchSize
trainingDataLength = (len(trainingSet) // batchSize) * batchSize
print(trainingDataLength)
trainingData = trainingSet[:trainingDataLength]
print("Length of cropped training data: " + str(len(trainingData)))
### do the network training
network.SGD(trainingData, 100, batchSize, 1, None)
network.saveToFile('BachGenerator1')
### generate a piece of music ----------------------------------------------
choraleArray = MusicHandler.getChoraleArray(0, 'jsbach_chorals_harmony.data')
choraleLength = 40
chords = []
chords.append(choraleArray[0])
chords.append(choraleArray[1])
chords.append(choraleArray[2])
chords.append(choraleArray[3])
for chord in range(choraleLength - 4):
input = [note for note in chords[chord]]
for i in range(1, 4):
input = input + chords[chord + i]
