Plot Confusion Matrix from CNN Model - machine-learning

Question

This original work is presented here
How to go about plotting the confusion matrix based of a CNN model?
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.optimizers import Adam
from keras.utils import np_utils
from sklearn import metrics
##Need to put this block of code in for cuDNN to initialize properly
import tensorflow as tf
config = tf.compat.v1.ConfigProto(gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=0.8)
# device_count = {'GPU': 1}
)
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config)
tf.compat.v1.keras.backend.set_session(session)
#------------------------------------------------------------------------------------------------------------------
num_rows = 40
num_columns = 174
num_channels = 1
x_train = x_train.reshape(x_train.shape[0], num_rows, num_columns, num_channels)
x_test = x_test.reshape(x_test.shape[0], num_rows, num_columns, num_channels)
num_labels = yy.shape[1]
filter_size = 2
# Construct model
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=2, input_shape=(num_rows, num_columns, num_channels), activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv2D(filters=32, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv2D(filters=64, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv2D(filters=128, kernel_size=2, activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(GlobalAveragePooling2D())
model.add(Dense(num_labels, activation='softmax'))
then trained as:
from keras.callbacks import ModelCheckpoint
from datetime import datetime
#num_epochs = 12
#num_batch_size = 128
num_epochs = 72
num_batch_size = 256
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.basic_cnn.hdf5',
verbose=1, save_best_only=True)
model.fit(x_train, y_train, batch_size=num_batch_size, epochs=num_epochs, validation_data=(x_test, y_test), callbacks=[checkpointer], verbose=1)
I have been trying a few things, one of which is:
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 4))
plot_confusion_matrix=(model(),x_test, y_test)
plt.plot(plot_confusion_matrix)
but I cannot get the confusion matrix to plot.
I also looked at tf.math.confusion_matrix(), but what is the labels and predictions as defined from the CNN model above??
The confusion matrix is a multi-classification.
Is
y_true = np.argmax(y_test, 1)??
and
y_pred = model.predict_classes(x_test)??

Answer 1

labels: 1-D Tensor of real labels for the classification task.
predictions: 1-D Tensor of predictions for a given classification.
As they say in official documentation , labels are the names of Output classes and predictions, However as they say everything has to be 1D tensor it means labels will be Ground truth for one instance and the corresponding indexed value in the Predictions will hold its predicted value.
So what you can do is, get the predictions and labels for each instances,in your code,you have passed the x_test and y_test which arent the supposed to be passed elements.
instead use model.predict to get the output labels.
y_predict=model.predict(x_test)
y_true=y_test
res = tf.math.confusion_matrix(y_true,y_predict)
This res is a 2D matrix now to print it you need to
plot_confusion_matrix(classifier, X_test, y_test,
display_labels=class_names,
cmap=plt.cm.Blues,
normalize=normalize)
Here put classifer = "model",not functional model().
Hope this helps,here are some more resources.
Here You can see the multiclass classification Confusion matrix technique.
Multiclass plot github function
Another custom plot function