# Training data
train_samples = []
for digit in range(10): # 0->9
for ten_by_ten_matrix in dataset.sample(5, digit, digit): # 5 x 'digit'
train_samples.append(ten_by_ten_matrix)
train_samples = np.asarray(train_samples).reshape(50, 100)
X_train = train_samples.astype('float32')
y_train = dtl.get_layer_output()[1]
Y_train = np_utils.to_categorical(y_train) # 50*10 one hot matrix (encoded outputs)
# Testing data
i = 0
test_samples = []
y_test = np.zeros((50, 1))
for digit in range(10): # 0->9
for ten_by_ten_matrix in dataset.new_sample(5, digit):
test_samples.append(ten_by_ten_matrix)
y_test[i] = digit
i += 1
test_samples = np.asarray(test_samples).reshape(50, 100)
X_test = test_samples.astype('float32')
Y_test = np_utils.to_categorical(y_test) # 50*10 one hot matrix (encoded outputs)
num_classes = Y_test.shape[1]
def keras_model():
# create model
model = Sequential()
# first hidden layer with 20 neurons
model.add(Dense(100, input_shape=(100,)))
model.add(Activation('relu'))
dataset = MNIST(n_components=100, reshape=False)
np.random.seed(7) # for reproducibility
max_voltage = 64
# Training data
x_train, y_train = dtl.get_layer_output()
X_train = x_train.astype('float32') / max_voltage # normalize
Y_train = np_utils.to_categorical(y_train) # 50*10 one hot matrix (encoded outputs)
# Testing data
y_test = np.array(y_train, copy=True)
x_test = np.array(x_train, copy=True)
new_x = []
for digit in range(10): # 0->9
for vector in dataset.new_sample(1, digit):
print('c**k {}'.format(digit))
voltages = dtl.classify(vector) # output of tempotron layer, list len 10
new_x.append(voltages)
y_test = np.append(y_test, [digit])
x_test = np.append(x_test, np.asarray(new_x)).reshape(len(x_test)+len(new_x), 10)
X_test = x_test.astype('float32') / max_voltage
Y_test = np_utils.to_categorical(y_test)
"""
np.random.seed(7) # for reproducibility
# load data, output matrices from tempotron
# With 50 training images and 50 testing images
