Help in adding confusion matrix

[Aashish]

Here is the final code ...its not working ..please help

import numpy as np 
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from sklearn.metrics import confusion_matrix

from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Convolution2D(32, 3, 3, input_shape = (32, 32, 3), activation = 'relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))


classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))


# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(output_dim = 128, activation = 'relu'))
classifier.add(Dense(output_dim = 10, activation = 'sigmoid'))

# Compiling the CNN
classifier.compile(optimizer = 'Adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Part 2 - Fitting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.4,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('Dataset/train',
                                                 target_size = (32,32),
                                                 batch_size = 64,
                                                 class_mode = 'categorical')
print('Before Test Set')

test_set = test_datagen.flow_from_directory('Dataset/test',
                                            target_size = (32,32),
                                            batch_size = 64,
                                            class_mode = 'categorical')

classifier.fit_generator(
        training_set,
        steps_per_epoch=2,
        epochs=1,
        validation_data=test_set,
        validation_steps=20)

print('After Epoch')
#Confution Matrix and Classification Report
Y_pred = classifier.predict_generator(test_set, 60000)
y_pred = np.argmax(Y_pred, axis=1)
print('Confusion Matrix')
print(confusion_matrix(test_set.classes, y_pred))
print('Classification Report')
target_names = ['Airplan','Car','Birds','Cats','Deer', 'Dogs','Frog', 'Horse','Ship','Truck']
print(classification_report(test_set.classes, y_pred, target_names=target_names))