Learning Data Mining with Python

Classifying Objects in Images Using Deep Learning
The last line, the break, is to test the code—this will drastically reduce the number of
training examples, allowing you to quickly see if your code is working. I'll prompt
you later to remove this line, after you have tested that the code works.
Next, create a dataset by stacking these batches on top of each other. We use
NumPy's vstack, which can be visualized as adding rows to the end of the array:
X = np.vstack([batch['data'] for batch in batches])
We then normalize the dataset to the range 0 to 1 and then force the type to be a
32-bit float (this is the only datatype the GPU-enabled virtual machine can run with):
X = np.array(X) / X.max()
X = X.astype(np.float32)
We then do the same with the classes, except we perform a hstack, which is similar
to adding columns to the end of the array. We then use the OneHotEncoder to turn
this into a one-hot array:
from sklearn.preprocessing import OneHotEncoder
y = np.hstack(batch['labels'] for batch in batches).flatten()
y = OneHotEncoder().fit_transform(y.reshape(y.shape[0],1)).todense()
y = y.astype(np.float32)
Next, we split the dataset into training and testing sets:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_
size=0.2)
Next, we reshape the arrays to preserve the original data structure. The original
data was 32 by 32 pixel images, with 3 values per pixel (for the red, green, and
blue values);
X_train = X_train.reshape(-1, 3, 32, 32)
X_test = X_test.reshape(-1, 3, 32, 32)
We now have a familiar training and testing dataset, along with the target classes
for each. We can now build the classifier.
Creating the neural network
We will be using the nolearn package to build the neural network, and therefore
will follow a pattern that is similar to our replication experiment in Chapter 8,
Beating CAPTCHAs with Neural Networks replication.
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