def print_for_model_digit(model, tag):
sess = tf.Session()
new_saver = tf.train.import_meta_graph(model + ".meta")
new_saver.restore(sess, model)
graph = tf.get_default_graph()
input_rec = graph.get_tensor_by_name('Reconstructor/input:0')
output_rec = graph.get_tensor_by_name('Reconstructor/output:0')
input_enc = graph.get_tensor_by_name('Encoder/input:0')
output_enc = graph.get_tensor_by_name('Encoder/output:0')
# r_cost = tf.losses.mean_squared_error(real_data_rot, output_enc)
for digit in range(10):
data = X_test_digits[digit]
z = sess.run([output_enc], feed_dict={input_enc: data})
z = z[0]
outf = open("score_%s_%i.txt" % (tag, digit), 'w')
for a in np.arange(-1, 1, 0.01):
angles = np.ones((len(data), 1)) * a
Xb_rot = rotatepair_batch.rotate_batch(data, angles[:, 0] * 180)
aco = np.cos(angles * math.pi)
asi = np.sin(angles * math.pi)
za = np.concatenate((z, aco, asi), axis=1)
samples = sess.run([output_rec], feed_dict={input_rec: za})
score = np.mean(np.square(Xb_rot - samples))
outf.write("%g %g\n" % (a, score))
print(tag, digit, a, score)
sess.close()
def get_batch(X, Y):
a = np.random.uniform(-1, 1, size=(BATCH_SIZE, ))
Xb, Yb = get_batch_only_Xb(X, Y)
sel_idx = (Yb == 4) | (Yb == 9)
a[sel_idx] /= 4
Xb_rot = rotatepair_batch.rotate_batch(Xb, a * 180)
return a, Xb, Xb_rot
def plot_for_model(model, tag):
sess = tf.Session()
new_saver = tf.train.import_meta_graph(model + ".meta")
new_saver.restore(sess, model)
graph = tf.get_default_graph()
input_img = graph.get_tensor_by_name('input_img:0')
output_img = graph.get_tensor_by_name('output_img:0')
for digit in range(10):
Xb = get_sample_test_digit(digit, BATCH_SIZE)
Xb = np.array(Xb)
csamples = []
creal = []
for a in np.arange(-1, 1, 0.1):
angles = np.ones(BATCH_SIZE) * a
Xb_rot = rotatepair_batch.rotate_batch(Xb, angles)
samples = sess.run(output_img, feed_dict={input_img: Xb_rot})
csamples.append(np.squeeze(samples))
creal.append(Xb_rot)
csamples = np.squeeze(csamples)
creal = np.squeeze(creal)
sh = list(csamples.shape)
sh[1] *= 2
cmerge = np.zeros(sh)
cmerge[:, 0::2] = creal
cmerge[:, 1::2] = csamples
save_plot_matrix(cmerge, 'rec_%s_%i.png' % (tag, digit))
sess.close()
def get_batch(X, Y):
a = np.random.uniform(-1, 1, size=(BATCH_SIZE, ))
#uncomment to learn the model on discrete angles to see it's interpolation abilities
#a = np.random.randint(-5, 5, size=(BATCH_SIZE,))
#a = np.divide(a, 5)
Xb, Yb = get_batch_only_Xb(X, Y)
sel_idx = (Yb == 4) | (Yb == 9)
a[sel_idx] /= 4
Xb_rot = rotatepair_batch.rotate_batch(Xb, a * 180)
return a, Xb, Xb_rot
def plot_for_model(model, tag):
sess = tf.Session()
new_saver = tf.train.import_meta_graph(model + ".meta")
new_saver.restore(sess, model)
graph = tf.get_default_graph()
input_rec = graph.get_tensor_by_name('Reconstructor/input:0')
output_rec = graph.get_tensor_by_name('Reconstructor/output:0')
input_enc = graph.get_tensor_by_name('Encoder/input:0')
output_enc = graph.get_tensor_by_name('Encoder/output:0')
for digit in range(10):
data = get_sample_test_digit(digit, BATCH_SIZE)
data = np.array(data)
z = sess.run([output_enc], feed_dict={input_enc: data})
z = z[0]
csamples = []
creal = []
for a in np.arange(-1, 1, 0.1):
angles = np.ones((BATCH_SIZE, 1)) * a
Xb_rot = rotatepair_batch.rotate_batch(data, angles[:, 0] * 180)
aco = np.cos(angles * math.pi)
asi = np.sin(angles * math.pi)
za = np.concatenate((z, aco, asi), axis=1)
samples = sess.run([output_rec], feed_dict={input_rec: za})
csamples.append(np.squeeze(samples))
creal.append(Xb_rot)
csamples = np.squeeze(csamples)
creal = np.squeeze(creal)
sh = list(csamples.shape)
sh[1] *= 2
cmerge = np.zeros(sh)
cmerge[:, 0::2] = creal
cmerge[:, 1::2] = csamples
save_plot_matrix(cmerge, 'rec_%s_%i.png' % (tag, digit))
sess.close()
def get_batch(X,Y):
a = np.random.uniform(-0.5, 0.5, size=(BATCH_SIZE,))
Xb,Yb = get_batch_only_Xb(X, Y)
Xb_rot = rotatepair_batch.rotate_batch(Xb, a*180)
return a, Xb, Xb_rot
