Autonomy Bootcamp PR - Annie Guo

README.md

@@ -1,3 +1,4 @@
 Repo for submitting Autonomy bootcamps.
 
 For more details see [Autonomy Bootcamp on Confluence](https://uwarg-docs.atlassian.net/wiki/spaces/BOOT/pages/1544290340/Autonomy+Bootcamp).
+

main.py

@@ -1,17 +1,136 @@
-"""
-This is a starter file to get you going. You may also include other files if you feel it's necessary.
+# The CIFAR-10 Dataset has 60,000 images and 10 classes
+# There are 50,000 training images and 10,000 test images
 
-Make sure to follow the code convention described here:
-https://github.com/UWARG/computer-vision-python/blob/main/README.md#naming-and-typing-conventions
+# Libraries
+import tensorflow as tf
+from tensorflow.keras import datasets, layers, models
+import matplotlib.pyplot as plt
+import numpy as np
 
-Hints:
-* The internet is your friend! Don't be afraid to search for tutorials/intros/etc.
-* We suggest using a convolutional neural network.
-* TensorFlow Keras has the CIFAR-10 dataset as a module, so you don't need to manually download and unpack it.
-"""
+# Load the CIFAR-10 dataset, returns 4 numpy arrays
+(images_train, labels_train), (images_test, labels_test) = datasets.cifar10.load_data()
 
-# Import whatever libraries/modules you need
+# For example, images_train is a numpy array containing the training images from the dataset.
+# It has a shape of (50000, 32, 32, 3)
+# 50,000 is the number of training samples
+# each sample is a 32 x 32 image
+# and there are 3 color channels (red, green, and blue)
+print("Training Image Details:", images_train.shape)
+# labels_train is a numpy array containing the corresponding labels for the training images. 
+# It has a shape of (50000)
+# each value is an integer representing the class label for the corresponding image in images_train
+print("Training Label Details:", labels_train.shape)
+# The same applies for the test images and labels
+print("Test Image Details:", images_test.shape) 
+print("Test Label Details:", labels_test.shape) 
 
-import numpy as np
+print("Numpy array representation of the first training image:")
+print(images_train[0])
+print()
+
+print("Visual representation of the first training image:")
+plt.imshow(images_train[0])
+
+# Quick code snippet for data exploration
+
+# Turn numpy array of labels into a one dimensional array.
+labels_train = labels_train.reshape(-1,)
+labels_test = labels_test.reshape(-1,)
+
+classes = ["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"]
+
+# x is a numpy array of training images
+# y is the corresponding numpy array of training labels
+# index specifies which training image to plot
+def plot_sample(x, y, index):
+    plt.imshow(x[index])
+    plt.xlabel(classes[y[index]])
+
+# Now we can easily plot some images
+plot_sample(images_train, labels_train, 175)
+plot_sample(images_test, labels_test, 12)
+plot_sample(images_test, labels_test, 28)
+
+# Normalize the data by dividing each pixel value in the images by 255
+# obtaining a number ranging from 0 to 1
+
+# Example:
+print("Normalized numpy array of the first image in training set")
+print(images_train[0] / 255)
+
+images_train = images_train / 255
+images_test = images_test / 255
+
+# Building a Convolutional Neural Network
+cnn = models.Sequential([
+    # CNN
+    layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)),
+    layers.MaxPooling2D((2, 2)),
+
+    layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),
+    layers.MaxPooling2D((2, 2)),
+
+    # Dense Layer
+    layers.Flatten(),
+    layers.Dense(64, activation='relu'),
+    layers.Dense(10, activation='softmax')
+])
+
+# Compile the model
+cnn.compile(optimizer='adam',
+            loss='sparse_categorical_crossentropy',
+            metrics=['accuracy'])
+
+# Train the model
+history = cnn.fit(images_train, labels_train, validation_data=(images_test, labels_test), epochs=8)
+
+# Graph for accuracy
+plt.plot(history.history['accuracy'])
+plt.plot(history.history['val_accuracy'])
+plt.title('Model Accuracy Plot')
+plt.ylabel('Accuracy')
+plt.xlabel('Epoch')
+plt.legend(['Train', 'Validation'], loc='upper left')
+plt.show()
+
+# Graph for loss
+plt.plot(history.history['loss'])
+plt.plot(history.history['val_loss'])
+plt.title('Model Loss Plot')
+plt.ylabel('Loss')
+plt.xlabel('Epoch')
+plt.legend(['Train', 'Validation'], loc='upper left')
+plt.show()
+
+# Evaluate the model's accuracy on test set
+print("Accuracy:")
+cnn.evaluate(images_test,labels_test)
+
+# Predict labels of training images
+probabilities = cnn.predict(images_test)
+
+# The predict() function returns an array of probabilities 
+# indicating the likelihood of each label for the test image at that index
+print("Probability Array:")
+print(probabilities)
+
+# The argmax function returns the index of the largest number in a list
+print("Arg Max Example:")
+print(np.argmax([2, 7, 5]))
+
+predictions = [np.argmax(p) for p in probabilities]
+
+# Extra: A function that determines if a model correctly predicted the label
+# for a specific image
+
+def correct(index):
+    if (predictions[index] == labels_test[index]):
+        print("Correct Prediction: ", classes[predictions[index]])
+    else:
+        print("Incorrect Prediction: ", classes[predictions[index]])
+        print("Actual: ", classes[labels_test[index]])
 
-# Your working code here
+correct(12)
+correct(0)
+correct(150)
+correct(28)

output.txt

@@ -0,0 +1,151 @@
+Training Image Details: (50000, 32, 32, 3)
+Training Label Details: (50000, 1)
+Test Image Details: (10000, 32, 32, 3)
+Test Label Details: (10000, 1)
+
+Numpy array representation of the first training image:
+[[[ 59  62  63]
+  [ 43  46  45]
+  [ 50  48  43]
+  ...
+  [158 132 108]
+  [152 125 102]
+  [148 124 103]]
+
+ [[ 16  20  20]
+  [  0   0   0]
+  [ 18   8   0]
+  ...
+  [123  88  55]
+  [119  83  50]
+  [122  87  57]]
+
+ [[ 25  24  21]
+  [ 16   7   0]
+  [ 49  27   8]
+  ...
+  [118  84  50]
+  [120  84  50]
+  [109  73  42]]
+
+ ...
+
+ [[208 170  96]
+  [201 153  34]
+  [198 161  26]
+  ...
+  [160 133  70]
+  [ 56  31   7]
+  [ 53  34  20]]
+
+ [[180 139  96]
+  [173 123  42]
+  [186 144  30]
+  ...
+  [184 148  94]
+  [ 97  62  34]
+  [ 83  53  34]]
+
+ [[177 144 116]
+  [168 129  94]
+  [179 142  87]
+  ...
+  [216 184 140]
+  [151 118  84]
+  [123  92  72]]]
+
+Normalized numpy array of the first image in training set
+[[[0.23137255 0.24313725 0.24705882]
+  [0.16862745 0.18039216 0.17647059]
+  [0.19607843 0.18823529 0.16862745]
+  ...
+  [0.61960784 0.51764706 0.42352941]
+  [0.59607843 0.49019608 0.4       ]
+  [0.58039216 0.48627451 0.40392157]]
+
+ [[0.0627451  0.07843137 0.07843137]
+  [0.         0.         0.        ]
+  [0.07058824 0.03137255 0.        ]
+  ...
+  [0.48235294 0.34509804 0.21568627]
+  [0.46666667 0.3254902  0.19607843]
+  [0.47843137 0.34117647 0.22352941]]
+
+ [[0.09803922 0.09411765 0.08235294]
+  [0.0627451  0.02745098 0.        ]
+  [0.19215686 0.10588235 0.03137255]
+  ...
+  [0.4627451  0.32941176 0.19607843]
+  [0.47058824 0.32941176 0.19607843]
+  [0.42745098 0.28627451 0.16470588]]
+
+ ...
+
+ [[0.81568627 0.66666667 0.37647059]
+  [0.78823529 0.6        0.13333333]
+  [0.77647059 0.63137255 0.10196078]
+  ...
+  [0.62745098 0.52156863 0.2745098 ]
+  [0.21960784 0.12156863 0.02745098]
+  [0.20784314 0.13333333 0.07843137]]
+
+ [[0.70588235 0.54509804 0.37647059]
+  [0.67843137 0.48235294 0.16470588]
+  [0.72941176 0.56470588 0.11764706]
+  ...
+  [0.72156863 0.58039216 0.36862745]
+  [0.38039216 0.24313725 0.13333333]
+  [0.3254902  0.20784314 0.13333333]]
+
+ [[0.69411765 0.56470588 0.45490196]
+  [0.65882353 0.50588235 0.36862745]
+  [0.70196078 0.55686275 0.34117647]
+  ...
+  [0.84705882 0.72156863 0.54901961]
+  [0.59215686 0.4627451  0.32941176]
+  [0.48235294 0.36078431 0.28235294]]]
+
+Epoch 1/8
+1563/1563 [==============================] - 11s 7ms/step - loss: 1.4798 - accuracy: 0.4666 - val_loss: 1.1862 - val_accuracy: 0.5801
+Epoch 2/8
+1563/1563 [==============================] - 10s 6ms/step - loss: 1.1120 - accuracy: 0.6114 - val_loss: 1.0751 - val_accuracy: 0.6328
+Epoch 3/8
+1563/1563 [==============================] - 10s 6ms/step - loss: 0.9803 - accuracy: 0.6614 - val_loss: 0.9805 - val_accuracy: 0.6622
+Epoch 4/8
+1563/1563 [==============================] - 10s 6ms/step - loss: 0.8941 - accuracy: 0.6905 - val_loss: 0.9274 - val_accuracy: 0.6862
+Epoch 5/8
+1563/1563 [==============================] - 10s 6ms/step - loss: 0.8276 - accuracy: 0.7125 - val_loss: 0.9045 - val_accuracy: 0.6902
+Epoch 6/8
+1563/1563 [==============================] - 10s 6ms/step - loss: 0.7719 - accuracy: 0.7326 - val_loss: 0.9064 - val_accuracy: 0.6914
+Epoch 7/8
+1563/1563 [==============================] - 10s 6ms/step - loss: 0.7241 - accuracy: 0.7488 - val_loss: 0.8675 - val_accuracy: 0.7082
+Epoch 8/8
+1563/1563 [==============================] - 10s 6ms/step - loss: 0.6857 - accuracy: 0.7612 - val_loss: 0.8900 - val_accuracy: 0.7066
+
+Accuracy:
+313/313 [==============================] - 1s 2ms/step - loss: 0.8900 - accuracy: 0.7066
+[0.8900312781333923, 0.70660001039505]
+
+Probability Array:
+[[7.9617519e-03 9.0055393e-05 9.4486173e-04 ... 1.6625634e-04
+  8.6174384e-02 5.7605947e-03]
+ [1.0169607e-01 1.0795504e-01 4.1591964e-04 ... 1.9064474e-07
+  7.8841889e-01 1.5102198e-03]
+ [2.2873239e-01 1.2059878e-01 8.9207612e-04 ... 2.4159673e-04
+  6.4592582e-01 3.0860878e-03]
+ ...
+ [1.0751112e-04 2.2846809e-06 4.8287667e-02 ... 7.2148964e-02
+  1.4690186e-05 1.2512889e-05]
+ [2.3069701e-01 5.4231435e-01 2.1159079e-02 ... 3.3880232e-04
+  1.0538092e-03 6.9458052e-03]
+ [5.2842511e-06 4.3653336e-06 1.2734244e-04 ... 9.9851340e-01
+  6.2807530e-08 2.6225247e-05]]
+
+Arg Max Example:
+1
+
+Correct Prediction:  dog
+Correct Prediction:  cat
+Incorrect Prediction:  automobile
+Actual:  ship
+Correct Prediction:  truck