corrections

as2_Maggioni_Claudio/deliverable/run_task1_bonus.py

@@ -0,0 +1,63 @@
+from tensorflow.keras.models import load_model
+import os
+import pickle
+import urllib.request as http
+from zipfile import ZipFile
+from tensorflow.keras import utils
+
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+from tensorflow.keras import layers as keras_layers
+from tensorflow.keras import backend as K
+from tensorflow.keras.datasets import cifar10
+from tensorflow.keras.models import save_model, load_model
+
+
+def load_cifar10(num_classes=3):
+    """
+    Downloads CIFAR-10 dataset, which already contains a training and test set,
+    and return the first `num_classes` classes.
+    Example of usage:
+
+    >>> (x_train, y_train), (x_test, y_test) = load_cifar10()
+
+    :param num_classes: int, default is 3 as required by the assignment.
+    :return: the filtered data.
+    """
+    (x_train_all, y_train_all), (x_test_all, y_test_all) = cifar10.load_data()
+
+    fil_train = tf.where(y_train_all[:, 0] < num_classes)[:, 0]
+    fil_test = tf.where(y_test_all[:, 0] < num_classes)[:, 0]
+
+    y_train = y_train_all[fil_train]
+    y_test = y_test_all[fil_test]
+
+    x_train = x_train_all[fil_train]
+    x_test = x_test_all[fil_test]
+
+    return (x_train, y_train), (x_test, y_test)
+
+if __name__ == '__main__':
+    
+    _, (x_test, y_test) = load_cifar10()
+    
+    x_test_n = x_test / 255
+    y_test_n = utils.to_categorical(y_test, 3)
+    
+    lrs = ["01", "0001"]
+    nns = [16, 64]
+    
+    for lr in lrs:
+        for nn in nns:
+            # Load the trained models
+            model_task1 = load_model('nn_task1_bonus/t1_bonus_%s_%d.h5' % (lr, nn))
+            
+            # Predict on the given samples
+            y_pred_task1 = model_task1.predict(x_test_n)
+
+            # Evaluate the missclassification error on the test set
+            assert y_test_n.shape == y_pred_task1.shape
+            test_loss, test_accuracy = model_task1.evaluate(x_test_n, y_test_n) # evaluate accuracy with proper function
+            print("Accuracy model task 1 (%d neurons, 0.%s learning rate):" % (nn, lr), test_accuracy)

as2_Maggioni_Claudio/report_Maggioni_Claudio.tex

@@ -103,6 +103,11 @@ The script will download automatically the models.
 Then, in order to run the models with the \texttt{run\_task*.py}, make sure you first \texttt{cd} in the 
 \texttt{deliverable} directory.
 
+Should the script not work, please download the compressed T1 and T2 models from 
+\url{https://drive.switch.ch/index.php/s/F0ubFgS6PBy8UM5/download} and uncompress the downloaded \texttt{.tar.gz}
+file in the \texttt{deliverable/} directory, and then make sure the directories \texttt{deliverable/nn\_task1} and \texttt{deliverable/nn\_task2}
+contain 3 \texttt{.h5} files in total.
+
 \maketitle
 
 In this assignment you are asked to:
@@ -180,6 +185,60 @@ The training and validation accuracy curves for the network is shown below:
 \caption{Training and validation accuracy curves during fitting for the CIFAR10 classifier}
 \end{figure}
 
+\subsection{Bonus}
+
+A plot of the validation loss and accuracy over training epochs for each of the configurations in the grid search are given below.
+The plot is useful to understand that for both learning rates chosen, choosing 64 neurons will lead to overfitting, due to the validation
+loss being higher than the training loss. This overfitting cannot be easily avoided with the adopted early stopping procedure, since
+our implementation monitors validation loss and not validation accuracy. 
+
+The second observation that can be found from the plots is that a smaller learning rate increases the number of epochs needed to achieve convergence
+but it also increases the model accuracy and decreases the model loss.
+
+By running all the models on the test set, using the script \texttt{run\_task1\_bonus.py} in the \texttt{deliverable/} directory,
+we obtain the following loss and accuracy:
+
+\begin{center}
+\begin{tabular}{cccc}
+\textbf{Learn rate} & \textbf{\# Neurons} & \textbf{Loss} & \textbf{Accuracy} \\\hline
+0.01 & 16 & 0.4597 & 0.8117 \\
+0.01 & 64 & 0.4508 & 0.8290 \\
+0.0001 & 16 & 0.4048 & 0.8437 \\
+0.0001 & 64 & 0.4073 & 0.8343 \\
+\end{tabular}
+\end{center}
+
+We choose the third configuration as the optimal one since it has highest accuracy (and lowest loss too). We then perform a T-test
+between this model and the one built in the previous section using the script \texttt{src/t\_test\_bonus.py}.
+
+The T-test gives a T-score of 1.374106, corresponding to a P-value of 0.169511, which makes us accept the null hypothesis therefore
+concluding that there is no significant difference between this optimal model and the one trained in the previous section.
+
+\begin{figure}[H]
+\centering
+	\resizebox{0.75\textwidth}{!}{%
+		\includegraphics[width=0.25\textwidth]{./t1_bonus_0001_16.png}}
+\caption{Training and validation accuracy curves during fitting for the CIFAR10 classifier (0.0001 learning rate, 16 neurons)}
+\end{figure}
+\begin{figure}[H]
+\centering
+	\resizebox{0.75\textwidth}{!}{%
+		\includegraphics[width=0.25\textwidth]{./t1_bonus_0001_64.png}}
+\caption{Training and validation accuracy curves during fitting for the CIFAR10 classifier (0.0001 learning rate, 64 neurons)}
+\end{figure}
+\begin{figure}[H]
+\centering
+	\resizebox{0.75\textwidth}{!}{%
+		\includegraphics[width=0.25\textwidth]{./t1_bonus_01_16.png}}
+\caption{Training and validation accuracy curves during fitting for the CIFAR10 classifier (0.01 learning rate, 16 neurons)}
+\end{figure}
+\begin{figure}[H]
+\centering
+	\resizebox{\textwidth}{!}{%
+		\includegraphics{./t1_bonus_01_64.png}}
+\caption{Training and validation accuracy curves during fitting for the CIFAR10 classifier (0.01 learning rate, 64 neurons)}
+\end{figure}
+
 %----------------------------------------------------------------------------------------
 %	Task 2
 %----------------------------------------------------------------------------------------

as2_Maggioni_Claudio/src/t1_bonus_0001_16.csv

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as2_Maggioni_Claudio/src/t_test_bonus.py

@@ -0,0 +1,22 @@
+import joblib
+import numpy as np
+from keras import models
+import scipy.stats
+
+# Import the accuracy of both models
+e_a = 0.856333315372467 # without augmentation
+e_b = 0.843666672706604 # with data augmentation
+
+# # of data points in both test sets
+L = 3000
+
+# Compute classification variance for both models 
+s_a = e_a * (1 - e_a)
+s_b = e_b * (1 - e_b)
+
+# Compute Student's T-test
+T = (e_a - e_b) / np.sqrt((s_a / L) + (s_b / L))
+print("T test:\t\t\t %1.06f" % T)
+print("P-value:\t\t %1.06f" % (scipy.stats.t.sf(abs(T), df=L) * 2))
+print("No aug variance:\t %1.06f" % s_a)
+print("With aug variance:\t %1.06f" % s_b)