What does the prediction say? CNN Keras

Question

I've created a CNN model to try to predict if the image is either a dog or a cat, but on the output I don't know what it predicted. See below:

import pandas as pd
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dense, Flatten, Conv2D, Dropout, MaxPooling2D
from scipy import misc
import numpy as np

def build_classifier():
    # Model based on 'https://www.researchgate.net/profile/Le_Lu/publication/277335071/figure/fig8/AS:294249976352779@1447166069905/Figure-8-The-proposed-CNN-model-architecture-is-composed-of-five-convolutional-layers.png'
    #It's smarter to add layer without creating variables because of the processing, but as a small dataset it doesn't matter a lot.
    classifier = Sequential()

    conv1 = Conv2D(filters=64, kernel_size=(2,2), activation='relu', input_shape=(64,64,3))
    conv2 = Conv2D(filters=192, kernel_size=(2,2), activation='relu')
    conv3 = Conv2D(filters=384, kernel_size=(2,2), activation='relu')
    conv4 = Conv2D(filters=256, kernel_size=(2,2), activation='relu')
    conv5 = Conv2D(filters=256, kernel_size=(2,2), activation='relu')
    pooling1 = MaxPooling2D(pool_size=(2,2))
    pooling2 = MaxPooling2D(pool_size=(2,2))
    pooling3 = MaxPooling2D(pool_size=(2,2))
    fcl1 = Dense(1024, activation='relu')
    fcl2 = Dense(1024, activation='relu')
    fcl3 = Dense(2, activation='softmax')
    dropout1= Dropout(0.5)
    dropout2 = Dropout(0.5)
    flatten = Flatten()

    layers = [conv1, pooling1, conv2, pooling2, conv3, conv4, conv5,
             pooling3, flatten, fcl1, dropout1, fcl2, dropout2, fcl3]

    for l in layers:
        classifier.add(l)

    return classifier

model = build_classifier()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

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

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        'dataset/training_set',
        target_size=(64, 64),
        batch_size=32,
        class_mode='categorical')

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


model.fit_generator(
        train_generator,
        steps_per_epoch=200,
        epochs=32,
        validation_data=validation_generator,
        validation_steps=100)

model.save('model.h5')
model.save_weights('model_weights.h5')

I opened my saved model in another file:

from keras.models import load_model
from scipy import misc
import numpy as np

def single_pred(filepath, model):
    classifier = load_model(model)
    img = misc.imread(filepath)
    img = misc.imresize(img, (64,64,3))
    img = np.expand_dims(img, 0)
    print(classifier.predict(img))

if __name__ == '__main__':
    single_pred('/home/leonardo/Desktop/Help/dataset/single_prediction/cat_or_dog_2.jpg', 'model.h5')

As output I get this:

Using TensorFlow backend.
2017-10-09 14:06:25.520018: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-09 14:06:25.520054: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
[[ 0.  1.]]

But how can I know if the prediction says that it is a dog or a cat? Having this result in hands I still don't know if the image is a dog or a cat.

Someone really needs to read some basic tutorial. Ignoring this will not be much fun in the future. Hint: look at your last layer fcl3 = Dense(2, activation='softmax'), your input shapes and your loss! — sascha
I did not say something's wrong. I hinted the 3 most important things in regards to interpreting the output. — sascha
fcl3 = Dense(2, activation='softmax'), means you are doing two class classification and [0 1] output says that it predicted the second class (1) whatever it is — patti_jane

ilan ilan · Accepted Answer · 2017-10-09T18:45:27

Unless you specify the labels, your generator will automatically create the categorical labels for you. You can inspect those using train_generator.class_indices The order of the class labels is alphanumeric, so cats=0 dogs=1

What does the prediction say? CNN Keras

1 Answers