i=20 plt.close('all') fig = plt.figure() ax1 = plt.subplot(311) plt.plot(seq[:,i]) plt.grid() ax1.set_title('input') ax2 = plt.subplot(312) plt.scatter(xrange(time_steps_y), targets[i], marker = 'o', c = 'b') plt.grid() a=seq[:,i] b=seq[:, i][:, np.newaxis] guess = model.gen_sample(b) aa=guess[1] guess=np.asarray(guess[0],dtype=np.float).reshape((10,3)) guessed_probs = plt.imshow(guess.T, interpolation = 'nearest', cmap = 'gray') ax2.set_title('blue points: true class, grayscale: model output (white mean class)') ax3 = plt.subplot(313) plt.plot(model.errors) plt.grid() ax3.set_title('Training error')
i = 20 plt.close('all') fig = plt.figure() ax1 = plt.subplot(311) plt.plot(seq[:, i]) plt.grid() ax1.set_title('input') ax2 = plt.subplot(312) plt.scatter(xrange(time_steps_y), targets[i], marker='o', c='b') plt.grid() a = seq[:, i] b = seq[:, i][:, np.newaxis] guess = model.gen_sample(b) aa = guess[1] guess = np.asarray(guess[0], dtype=np.float).reshape((10, 3)) guessed_probs = plt.imshow(guess.T, interpolation='nearest', cmap='gray') ax2.set_title( 'blue points: true class, grayscale: model output (white mean class)') ax3 = plt.subplot(313) plt.plot(model.errors) plt.grid() ax3.set_title('Training error') '''
else: model.load('encdec_new.pkl') i = 20 plt.close('all') fig = plt.figure() ax1 = plt.subplot(311) plt.plot(seq[i]) plt.grid() ax1.set_title('input') ax2 = plt.subplot(312) plt.scatter(xrange(time_steps_y), targets[i], marker='o', c='b') plt.grid() guess = model.gen_sample(seq[i]) aa = guess[1] guess = np.asarray(guess[0], dtype=np.float).reshape((10, 3)) guessed_probs = plt.imshow(guess.T, interpolation='nearest', cmap='gray') ax2.set_title( 'blue points: true class, grayscale: model output (white mean class)') ax3 = plt.subplot(313) plt.plot(model.errors) plt.grid() ax3.set_title('Training error') '''
else: model.load("encdec_new.pkl") i = 20 plt.close("all") fig = plt.figure() ax1 = plt.subplot(311) plt.plot(seq[i]) plt.grid() ax1.set_title("input") ax2 = plt.subplot(312) plt.scatter(xrange(time_steps_y), targets[i], marker="o", c="b") plt.grid() guess = model.gen_sample(seq[i]) aa = guess[1] guess = np.asarray(guess[0], dtype=np.float).reshape((10, 3)) guessed_probs = plt.imshow(guess.T, interpolation="nearest", cmap="gray") ax2.set_title("blue points: true class, grayscale: model output (white mean class)") ax3 = plt.subplot(313) plt.plot(model.errors) plt.grid() ax3.set_title("Training error")