chore: import upstream snapshot with attribution

lessons/4-ComputerVision/08-TransferLearning/tfcv.py

@@ -0,0 +1,86 @@
+# Tensorflow Computer Vision Helper
+
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+import glob
+import os
+
+def plot_convolution(data,t,title=''):
+    fig, ax = plt.subplots(2,len(data)+1,figsize=(8,3))
+    fig.suptitle(title,fontsize=16)
+    tt = np.expand_dims(np.expand_dims(t,2),2)
+    for i,im in enumerate(data):
+        ax[0][i].imshow(im)
+        ximg = np.expand_dims(np.expand_dims(im,2),0)
+        cim = tf.nn.conv2d(ximg,tt,1,'SAME')
+        ax[1][i].imshow(cim[0][:,:,0])
+        ax[0][i].axis('off')
+        ax[1][i].axis('off')
+    ax[0,-1].imshow(t)
+    ax[0,-1].axis('off')
+    ax[1,-1].axis('off')
+    #plt.tight_layout()
+    plt.show()
+
+def plot_results(hist):
+    fig,ax = plt.subplots(1,2,figsize=(15,3))
+    ax[0].set_title('Accuracy')
+    ax[1].set_title('Loss')
+    for x in ['acc','val_acc']:
+        ax[0].plot(hist.history[x])
+    for x in ['loss','val_loss']:
+        ax[1].plot(hist.history[x])
+    plt.show()
+
+def display_dataset(dataset, labels=None, n=10, classes=None):
+    fig,ax = plt.subplots(1,n,figsize=(15,3))
+    for i in range(n):
+        ax[i].imshow(dataset[i])
+        ax[i].axis('off')
+        if classes is not None and labels is not None:
+            ax[i].set_title(classes[labels[i][0]])
+
+def check_image(fn):
+    try:
+        im = Image.open(fn)
+        im.verify()
+        return im.format=='JPEG'
+    except:
+        return False
+    
+def check_image_dir(path):
+    for fn in glob.glob(path):
+        if not check_image(fn):
+            print("Corrupt image or wrong format: {}".format(fn))
+            os.remove(fn)
+
+def load_cats_dogs_dataset(batch_size=64):
+    if not os.path.exists('data/PetImages'):
+        print("Extracting the dataset")
+        with zipfile.ZipFile('data/kagglecatsanddogs_3367a.zip', 'r') as zip_ref:
+            zip_ref.extractall('data')
+    print("Checking dataset")
+    check_image_dir('data/PetImages/Cat/*.jpg')
+    check_image_dir('data/PetImages/Dog/*.jpg')
+    data_dir = 'data/PetImages'
+    print("Loading dataset")
+    ds_train = keras.preprocessing.image_dataset_from_directory(
+        data_dir,
+        validation_split = 0.2,
+        subset = 'training',
+        seed = 13,
+        image_size = (224,224),
+        batch_size = batch_size
+    )
+    ds_test = keras.preprocessing.image_dataset_from_directory(
+        data_dir,
+        validation_split = 0.2,
+        subset = 'validation',
+        seed = 13,
+        image_size = (224,224),
+        batch_size = batch_size
+    )
+    return ds_train,ds_test