multiclass_clasyfication_resnet_trainscript2.py
def plot_confusion_matrix(cm, save_path,
                          target_names,
                          title='Confusion matrix',
                          cmap=None,
                          normalize=False):
    """
    given a sklearn confusion matrix (cm), make a nice plot
    Arguments
    ---------
    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:         the gradient of the values displayed from matplotlib.pyplot.cm
                  see http://matplotlib.org/examples/color/colormaps_reference.html
                  plt.get_cmap('jet') or plt.cm.Blues
    normalize:    If False, plot the raw numbers
                  If True, plot the proportions
    Usage
    -----
    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by
                                                              # sklearn.metrics.confusion_matrix
                          normalize    = True,                # show proportions
                          target_names = y_labels_vals,       # list of names of the classes
                          title        = best_estimator_name) # title of graph
    Citiation
    ---------
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

    """
    accuracy = np.trace(cm) / np.sum(cm).astype('float')
    misclass = 1 - accuracy
    if cmap is None:
        cmap = plt.get_cmap('Blues')
    fig = plt.figure(figsize=(8, 6))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    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))
    fig.savefig(save_path, dpi=fig.dpi)
    return fig

    given a sklearn confusion matrix (cm), make a nice plot
    Arguments
    ---------
    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:         the gradient of the values displayed from matplotlib.pyplot.cm
                  see http://matplotlib.org/examples/color/colormaps_reference.html
                  plt.get_cmap('jet') or plt.cm.Blues
    normalize:    If False, plot the raw numbers
                  If True, plot the proportions
    Usage
    -----
    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by
                                                              # sklearn.metrics.confusion_matrix
                          normalize    = True,                # show proportions
                          target_names = y_labels_vals,       # list of names of the classes
                          title        = best_estimator_name) # title of graph
    Citiation
    ---------
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

    
given a sklearn confusion matrix (cm), make a nice plot
given a sklearn confusion matrix ( cm ) make a nice plot
def plot_confusion_matrix(cm, save_path,
                          target_names,
                          title='Confusion matrix',
                          cmap=None,
                          normalize=False):
    accuracy = np.trace(cm) / np.sum(cm).astype('float')
    misclass = 1 - accuracy
    if cmap is None:
        cmap = plt.get_cmap('Blues')
    fig = plt.figure(figsize=(8, 6))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    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))
    fig.savefig(save_path, dpi=fig.dpi)
    return fig
def plot_confusion_matrix ( cm , save_path , target_names , title = 'Confusion matrix' , cmap = None , normalize = False ) : accuracy = np . trace ( cm ) / np . sum ( cm ) . astype ( 'float' ) misclass = 1 - accuracy if cmap is None : cmap = plt . get_cmap ( 'Blues' ) fig = plt . figure ( figsize = ( 8 , 6 ) ) plt . imshow ( cm , interpolation = 'nearest' , cmap = cmap ) 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 ) ) fig . savefig ( save_path , dpi = fig . dpi ) return fig
given a sklearn confusion matrix (cm), make a nice plot
given a sklearn confusion matrix ( cm ) make a nice plot
"""
    given a sklearn confusion matrix (cm), make a nice plot
    Arguments
    ---------
    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:         the gradient of the values displayed from matplotlib.pyplot.cm
                  see http://matplotlib.org/examples/color/colormaps_reference.html
                  plt.get_cmap('jet') or plt.cm.Blues
    normalize:    If False, plot the raw numbers
                  If True, plot the proportions
    Usage
    -----
    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by
                                                              # sklearn.metrics.confusion_matrix
                          normalize    = True,                # show proportions
                          target_names = y_labels_vals,       # list of names of the classes
                          title        = best_estimator_name) # title of graph
    Citiation
    ---------
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

    """
cm
None
save_path
target_names
the class names, for example: ['high', 'medium', 'low']
the class names for example : [ ' high ' ' medium ' ' low ' ]
title
None
cmap
None
normalize
If True, plot the proportions
If True plot the proportions
Usage

None
-----

None
plot_confusion_matrix(cm           = cm,                  # confusion matrix created by

sklearn.metrics.confusion_matrix
normalize    = True,                # show proportions
target_names = y_labels_vals,       # list of names of the classes
title        = best_estimator_name) # title of graph
sklearn . metrics . confusion_matrix normalize = True # show proportions target_names = y_labels_vals # list of names of the classes title = best_estimator_name ) # title of graph
Citiation

None
---------

None
http
None