First version of CNN :-)

Former-commit-id: 10afacbc

GTSRB/data/dataset.tar.gz.REMOVED.git-id

-ba1540d488f9ab5e4c8b7eb062515a54ebc93704
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+7bb54a6fdefd74be4d659322f77a90cbba9757ec
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GTSRB/deepmods/__init__.py

+
+VERSION='0.1a'
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GTSRB/deepmods/pwk.py

+
+# ==================================================================
+#  ____                 _   _           _  __        __         _
+# |  _ \ _ __ __ _  ___| |_(_) ___ __ _| | \ \      / /__  _ __| | __
+# | |_) | '__/ _` |/ __| __| |/ __/ _` | |  \ \ /\ / / _ \| '__| |/ /
+# |  __/| | | (_| | (__| |_| | (_| (_| | |   \ V  V / (_) | |  |   <
+# |_|   |_|  \__,_|\___|\__|_|\___\__,_|_|    \_/\_/ \___/|_|  |_|\_\
+#                                                        module pwk                                   
+# ==================================================================
+# A simple module to host some common functions for practical work
+# pjluc 2019
+
+import os
+import glob
+from datetime import datetime
+import itertools
+import datetime
+
+import math
+import numpy as np
+
+import tensorflow as tf
+from tensorflow import keras
+
+import matplotlib
+import matplotlib.pyplot as plt
+
+VERSION='0.1.0'
+
+
+# -------------------------------------------------------------
+# init_all
+# -------------------------------------------------------------
+#
+def init(mplstyle='deepmods/talk.mplstyle'):
+    global VERSION
+    # ---- matplotlib
+    matplotlib.style.use(mplstyle)
+    # ---- Hello world
+    now = datetime.datetime.now()
+    print('\nDeepmod/pwk by pjluc 2019')
+    print('  Version            :', VERSION)
+    print('  Run time           : {}'.format(now.strftime("%A %-d %B %Y, %H:%M:%S")))
+    print('  Matplotlib style   :', mplstyle)
+    print('  TensorFlow version : ',tf.__version__)
+    print('  Keras version      : ',tf.keras.__version__)
+          
+# -------------------------------------------------------------
+# init_folder
+# -------------------------------------------------------------
+#
+def init_folder(path):
+    os.makedirs(path, mode=0o750, exist_ok=True)
+      
+
+# -------------------------------------------------------------
+# shuffle_dataset
+# -------------------------------------------------------------
+#
+def shuffle_dataset(x, y):
+    assert (len(x) == len(y)), "x and y must have same size"
+    p = np.random.permutation(len(x))
+    return x[p], y[p]
+
+
+def update_progress(what,i,imax):
+    bar_length = 40
+    if (i%int(imax/bar_length))!=0 and i<imax:
+        return
+    progress  = float(i/imax)
+    block     = int(round(bar_length * progress))
+    endofline = '\r' if progress<1 else '\n'
+    text = "{:16s} [{}] {:.1f}%".format( what, "#"*block+"-"*(bar_length-block), progress*100)
+    print(text, end=endofline)
+
+
+# -------------------------------------------------------------
+# show_images
+# -------------------------------------------------------------
+#
+def plot_images(x,y, indices, columns=12, x_size=1, y_size=1, colorbar=False, y_pred=None, cm='binary'):
+    """
+    Show some images in a grid, with legends
+    args:
+        X: images
+        y: real classes
+        indices: indices of images to show
+        columns: number of columns (12)
+        x_size,y_size: figure size
+        colorbar: show colorbar (False)
+        y_pred: predicted classes (None)
+        cm: Matplotlib olor map
+    returns: 
+        nothing
+    """
+    rows    = math.ceil(len(indices)/columns)
+    fig=plt.figure(figsize=(columns*x_size, rows*(y_size+0.35)))
+    n=1
+    errors=0 
+    if np.any(y_pred)==None:
+        y_pred=y
+    for i in indices:
+        axs=fig.add_subplot(rows, columns, n)
+        n+=1
+        img=axs.imshow(x[i],cmap = cm, interpolation='lanczos')
+        img=axs.imshow(x[i],cmap = cm)
+        axs.spines['right'].set_visible(True)
+        axs.spines['left'].set_visible(True)
+        axs.spines['top'].set_visible(True)
+        axs.spines['bottom'].set_visible(True)
+        axs.set_yticks([])
+        axs.set_xticks([])
+        if y[i]!=y_pred[i]:
+            axs.set_xlabel('{} ({})'.format(y_pred[i],y[i]))
+            axs.xaxis.label.set_color('red')
+            errors+=1
+        else:
+            axs.set_xlabel(y[i])
+        if colorbar:
+            fig.colorbar(img,orientation="vertical", shrink=0.65)
+    plt.show()
+  
+
+# -------------------------------------------------------------
+# show_history
+# -------------------------------------------------------------
+#
+def plot_history(history, figsize=(8,6)):
+    """
+    Show history
+    args:
+        history: history
+        save_as: filename to save or None
+    """
+    # Accuracy 
+    plt.figure(figsize=figsize)
+    plt.plot(history.history['acc'])
+    plt.plot(history.history['val_acc'])
+    plt.title('Model accuracy')
+    plt.ylabel('Accuracy')
+    plt.xlabel('Epoch')
+    plt.legend(['Train', 'Test'], loc='upper left')
+    if save_as!=None:
+        save_fig(save_as+'-acc', svg=False)
+    plt.show()
+
+    # Loss values
+    plt.figure(figsize=figsize)
+    plt.plot(history.history['loss'])
+    plt.plot(history.history['val_loss'])
+    plt.title('Model loss')
+    plt.ylabel('Loss')
+    plt.xlabel('Epoch')
+    plt.legend(['Train', 'Test'], loc='upper left')
+    plt.show()    
+
+
+def plot_confusion_matrix(cm,
+                          target_names,
+                          title='Confusion matrix',
+                          figsize=(8,6),
+                          cmap=None,
+                          normalize=True):
+    """
+    given a sklearn confusion matrix (cm), make a nice plot
+
+    Args:
+        cm:           confusion matrix from sklearn.metrics.confusion_matrix
+        target_names: given classification classes such as [0, 1, 2]
+                      the class names, for example: ['high', 'medium', 'low']
+        title:        the text to display at the top of the matrix
+        cmap:         color map
+        normalize:    False : plot raw numbers, True: plot proportions
+        save_as:      If not None, filename to save
+
+    Citiation:
+        http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
+
+    """
+ 
+    accuracy = np.trace(cm) / float(np.sum(cm))
+    misclass = 1 - accuracy
+
+    if (figsize[0]==figsize[1]):
+        aspect='equal'
+    else:
+        aspect='auto'
+
+    if cmap is None:
+        cmap = plt.get_cmap('Blues')
+
+    plt.figure(figsize=figsize)
+    plt.imshow(cm, interpolation='nearest', cmap=cmap, aspect=aspect)
+    plt.title(title)
+    plt.colorbar()
+
+    if target_names is not None:
+        tick_marks = np.arange(len(target_names))
+        plt.xticks(tick_marks, target_names, rotation=45)
+        plt.yticks(tick_marks, target_names)
+
+    if normalize:
+        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+
+
+    thresh = cm.max() / 1.5 if normalize else cm.max() / 2
+    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+        if normalize:
+            plt.text(j, i, "{:0.4f}".format(cm[i, j]),
+                     horizontalalignment="center",
+                     color="white" if cm[i, j] > thresh else "black")
+        else:
+            plt.text(j, i, "{:,}".format(cm[i, j]),
+                     horizontalalignment="center",
+                     color="white" if cm[i, j] > thresh else "black")
+
+
+    # plt.tight_layout()
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
+
+    plt.show()

GTSRB/deepmods/talk.mplstyle

+
+# See : https://matplotlib.org/users/customizing.html
+
+axes.titlesize : 24
+axes.labelsize : 20
+axes.edgecolor      : dimgrey
+axes.labelcolor     : dimgrey
+axes.linewidth      : 2
+axes.grid           : False
+
+axes.prop_cycle    : cycler('color', ['steelblue', 'tomato', '2ca02c', 'd62728', '9467bd', '8c564b', 'e377c2', '7f7f7f', 'bcbd22', '17becf'])
+
+lines.linewidth     : 3
+lines.markersize    : 10
+
+xtick.color         : black
+xtick.labelsize     : 18
+ytick.color         : black
+ytick.labelsize     : 18
+
+axes.spines.left   : True
+axes.spines.bottom : True
+axes.spines.top    : False
+axes.spines.right  : False
+
+savefig.dpi         : 300      # figure dots per inch or 'figure'
+savefig.facecolor   : white    # figure facecolor when saving
+savefig.edgecolor   : white    # figure edgecolor when saving
+savefig.format      : svg
+savefig.bbox        : tight
+savefig.pad_inches  : 0.1
+savefig.transparent : True
+savefig.jpeg_quality: 95