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model = Sequential()
# Tier one 
model.add(Conv2D(32, kernel_size=5, input_shape = (28, 28, 1), activation='relu', padding = 'Same' ))
model.add(Conv2D(64, kernel_size=5, activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Dropout(0.33))

model.add(Conv2D(128, kernel_size=3, activation='relu'))
model.add(Conv2D(256, kernel_size=3, activation = 'relu'))

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from sklearn.tree import DecisionTreeClassifier
myClassifier2 = DecisionTreeClassifier(max_depth = 5, min_samples_leaf = 2)
myClassifier2.fit(X_train, y_train)
predictions2 = myClassifier2.predict(X_test)

cnf2 = confusion_matrix(y_test, predictions2)
score2 = accuracy_score(y_test, predictions2)

print ("Confusion Matrix for our Decision Tree classifier is :\n ", cnf2)

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# Importing the tasty stuff
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.externals import joblib

X = finalDf['size_in_mb']
y = finalDf['pop_categories']

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A warning occurred (42 apples)
An error occurred