def load_instances(sFilename, cMaxInstances=None): return nn.load_data(data_filename(sFilename), cMaxInstances)
def load_data(): (x_train, y_train), (x_test, y_test) = nn.load_data() x_train = np.divide(x_train, 255.) x_test = np.divide(x_test, 255.) return (x_train, y_train), (x_test, y_test)
def load_data(): (x_train, y_train), (x_test, y_test) = nn.load_data() x_train = np.expand_dims(np.divide(x_train, 255.), -1) x_test = np.expand_dims(np.divide(x_test, 255.), -1) return (x_train, y_train), (x_test, y_test)
#!/usr/bin/python import nn n = nn.read_from_file('net.pic') d = nn.load_data('test_img') f0 = [nn.feed_forward(n, q.listDblFeatures)[0] for q in d if q.iLabel == 0] f1 = [nn.feed_forward(n, q.listDblFeatures)[0] for q in d if q.iLabel == 1] print >>open('d0', 'w'), '\n'.join(map(str, sorted(f0))) print >>open('d1', 'w'), '\n'.join(map(str, sorted(f1)))
from nn import load_data def slicer(arr, slice_size): i = 0 assert arr.shape[0] >= slice_size while True: arr_slice = arr[i:i+slice_size, :] if arr_slice.shape[0] < slice_size: i = 0 continue i += slice_size yield arr_slice X, Y, test = load_data() sess = tf.Session() x = tf.placeholder(tf.float32, [None, 784]) w = tf.Variable(tf.zeros([784, 10])) y = tf.nn.softmax(tf.matmul(x, w)) y_ = tf.placeholder(tf.float32, [None, 10]) max_w_op = tf.reduce_max(w) min_w_op = tf.reduce_min(w) correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) sq_err = tf.scalar_mul(1/2, tf.reduce_sum(tf.square(y - y_)))
def load_data(): (x_train, y_train), (x_test, y_test) = nn.load_data() x_train = np.divide(x_train, 255.).reshape([-1, 784]) x_test = np.divide(x_test, 255.).reshape([-1, 784]) return (x_train, y_train), (x_test, y_test)
#!/usr/bin/python import nn n = nn.read_from_file('net.pic') d = nn.load_data('test_img') f0 = [nn.feed_forward(n, q.listDblFeatures)[0] for q in d if q.iLabel == 0] f1 = [nn.feed_forward(n, q.listDblFeatures)[0] for q in d if q.iLabel == 1] print >> open('d0', 'w'), '\n'.join(map(str, sorted(f0))) print >> open('d1', 'w'), '\n'.join(map(str, sorted(f1)))