intro_to_cnns

ShadowEditor.AI/README.md

@@ -37,6 +37,7 @@ pip install tensorflow-gpu==2.0.0-rc0
 8. tensorflow/basic/basic_regression.py: 线性回归
 9. tensorflow/basic/overfit_and_underfit.py: 过拟合和欠拟合，准确度：99.99%
 10. tensorflow/basic/save_and_restore_models.py: 保存和恢复模型
+11. tensorflow/basic/intro_to_cnns.py: 卷积神经网络
 
 ## 相关地址
 

ShadowEditor.AI/tensorflow/basic/hub_with_keras.py

@@ -0,0 +1,52 @@
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import matplotlib.pylab as plt
+
+import tensorflow as tf
+
+import tensorflow_hub as hub
+
+from tensorflow.keras import layers
+
+classifier_url ="https://hub.tensorflow.google.cn/google/tf2-preview/mobilenet_v2/classification/2" #@param {type:"string"}
+
+IMAGE_SHAPE = (224, 224)
+
+classifier = tf.keras.Sequential([
+    hub.KerasLayer(classifier_url, input_shape=IMAGE_SHAPE+(3,))
+])
+
+import numpy as np
+import PIL.Image as Image
+
+grace_hopper = tf.keras.utils.get_file('image.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg')
+grace_hopper = Image.open(grace_hopper).resize(IMAGE_SHAPE)
+
+grace_hopper = np.array(grace_hopper)/255.0
+
+result = classifier.predict(grace_hopper[np.newaxis, ...])
+
+predicted_class = np.argmax(result[0], axis=-1)
+
+labels_path = tf.keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')
+imagenet_labels = np.array(open(labels_path).read().splitlines())
+
+plt.imshow(grace_hopper)
+plt.axis('off')
+predicted_class_name = imagenet_labels[predicted_class]
+_ = plt.title("Prediction: " + predicted_class_name.title())
+
+# data_root = tf.keras.utils.get_file(
+#   'flower_photos','https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
+#    untar=True)
+
+# image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1/255)
+# image_data = image_generator.flow_from_directory(str(data_root), target_size=IMAGE_SHAPE)
+
+# for image_batch, label_batch in image_data:
+#   print("Image batch shape: ", image_batch.shape)
+#   print("Label batch shape: ", label_batch.shape)
+#   break
+
+# result_batch = classifier.predict(image_batch)
+

ShadowEditor.AI/tensorflow/basic/intro_to_cnns.py

@@ -0,0 +1,37 @@
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import tensorflow as tf
+
+from tensorflow.keras import datasets, layers, models
+
+(train_images, train_labels), (test_images,
+                               test_labels) = datasets.mnist.load_data()
+
+train_images = train_images.reshape((60000, 28, 28, 1))
+test_images = test_images.reshape((10000, 28, 28, 1))
+
+# Normalize pixel values to be between 0 and 1
+train_images, test_images = train_images / 255.0, test_images / 255.0
+
+model = models.Sequential()
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+
+model.summary()
+
+model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(10, activation='softmax'))
+
+model.summary()
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.fit(train_images, train_labels, epochs=5)
+
+test_loss, test_acc = model.evaluate(test_images, test_labels)