Kanggo ngowahi kode kanggo nampilake gambar sing diowahi ukurane ing format kothak, kita bisa nggunakake perpustakaan matplotlib ing Python. Matplotlib minangka perpustakaan plot sing akeh digunakake sing nyedhiyakake macem-macem fungsi kanggo nggawe visualisasi.
Kaping pisanan, kita kudu ngimpor perpustakaan sing dibutuhake. Saliyane TensorFlow, kita bakal ngimpor modul matplotlib.pyplot minangka plt:
python import tensorflow as tf import matplotlib.pyplot as plt
Sabanjure, kita kudu ngowahi kode kanggo ngowahi ukuran gambar. Yen kita duwe dhaptar gambar sing disimpen ing variabel sing diarani `gambar`, kita bisa nggunakake fungsi `tf.image.resize ()` TensorFlow kanggo ngowahi ukuran saben gambar menyang wangun sing dikarepake. Contone, yen kita pengin ngowahi ukuran gambar menyang wangun (64, 64), kita bisa nindakake ing ngisor iki:
python resized_images = [tf.image.resize(image, (64, 64)) for image in images]
Saiki kita duwe gambar sing diowahi ukurane, kita bisa nggawe tata letak kothak kanggo nampilake. Kita bakal nggunakake fungsi `plt.subplots ()` kanggo nggawe kothak subplots, ngendi saben subplot nggantosi gambar. Kita bisa nemtokake jumlah larik lan kolom ing kothak, uga ukuran saben subplot:
python num_rows = 4 num_cols = 4 fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, 10))
Sabanjure, kita bisa ngulang gambar sing diowahi ukurane lan plot saben gambar ing subplot. Kita bisa nggunakake fungsi `imshow()` saka obyek `Axes` kanggo nampilake gambar:
python for i, ax in enumerate(axes.flat): ax.imshow(resized_images[i]) ax.axis('off')
Pungkasan, kita bisa nggunakake fungsi `plt.show()` kanggo nampilake kothak gambar:
python plt.show()
Nggabungake kabeh, kode sing diowahi kanggo nampilake gambar sing diowahi ukurane ing format kothak bakal katon kaya iki:
python import tensorflow as tf import matplotlib.pyplot as plt # Assuming we have a list of images stored in the variable `images` resized_images = [tf.image.resize(image, (64, 64)) for image in images] # Create a grid layout for the images num_rows = 4 num_cols = 4 fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, 10)) # Plot each resized image on a subplot for i, ax in enumerate(axes.flat): ax.imshow(resized_images[i]) ax.axis('off') # Display the grid of images plt.show()
Kanthi tindakake langkah iki, sampeyan bisa ngowahi kode kanggo nampilake gambar sing diowahi ukurane ing format kothak nggunakake perpustakaan matplotlib ing Python.
Pitakonan lan jawaban anyar liyane babagan Jaringan saraf 3D convolional kanthi kompetisi deteksi kanker paru-paru Kaggle:
- Apa sawetara tantangan lan pendekatan potensial kanggo ningkatake kinerja jaringan saraf konvolusional 3D kanggo deteksi kanker paru-paru ing kompetisi Kaggle?
- Kepiye carane bisa ngitung jumlah fitur ing jaringan syaraf konvolusional 3D, kanthi nimbang dimensi patch konvolusi lan jumlah saluran?
- Apa tujuan padding ing jaringan saraf convolutional, lan apa pilihan kanggo padding ing TensorFlow?
- Kepiye jaringan saraf konvolusional 3D beda karo jaringan 2D babagan dimensi lan langkah?
- Apa langkah-langkah kanggo mbukak jaringan saraf konvolusional 3D kanggo kompetisi deteksi kanker paru-paru Kaggle nggunakake TensorFlow?
- Apa tujuane nyimpen data gambar menyang file numpy?
- Kepiye proses preprocessing dilacak?
- Apa pendekatan sing disaranake kanggo preprocessing dataset sing luwih gedhe?
- Apa tujuane ngowahi label dadi format siji-panas?
- Apa paramèter saka fungsi "process_data" lan apa nilai standar?
Pitakon lan jawaban liyane:
- Lapangan: Kacerdhasan gawéyan
- program: Sinau jero EITC/AI/DLTF kanthi TensorFlow (pindhah menyang program sertifikasi)
- Pawulangan: Jaringan saraf 3D convolional kanthi kompetisi deteksi kanker paru-paru Kaggle (pindhah menyang pelajaran sing gegandhengan)
- Topik: Nggambarake (pindhah menyang topik sing gegandhengan)
- Review ujian