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# UrusuLambda/tensorflow_middle_output.py

Created May 13, 2018
 canvas_orig = np.empty((28 * n, 28 * n)) canvas_recon = np.empty((28 * n, 28 * n)) canvas_middle = np.empty((16 * n, 8 * n)) for i in range(n): # MNIST test set batch_x, _ = mnist.test.next_batch(n) # ここで中間層の出力値を取得することができる # 取り出しはここが全て m = sess.run(encoder_op, feed_dict={X: batch_x}) # Encode and decode the digit image g = sess.run(decoder_op, feed_dict={X: batch_x}) # Display original images for j in range(n): # Draw the original digits canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = batch_x[j].reshape([28, 28]) # Display reconstructed images for j in range(n): # Draw the middle digits canvas_middle[i * 16:(i + 1) * 16, j * 8:(j + 1) * 8] = m[j].reshape([16, 8]) # Display reconstructed images for j in range(n): # Draw the reconstructed digits canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28]) print("Original Images") plt.figure(figsize=(n, n)) plt.imshow(canvas_orig, origin="upper", cmap="gray") plt.show() print("Middle Images") plt.figure(figsize=(n, n)) plt.imshow(canvas_middle, origin="upper", cmap="gray") plt.show() print("Reconstructed Images") plt.figure(figsize=(n, n)) plt.imshow(canvas_recon, origin="upper", cmap="gray") plt.show()
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