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run_ann.py
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run_ann.py
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import keras
from keras.models import Sequential
from keras import layers
import pandas
import numpy
dataset = pandas.read_csv("dataset.csv")
#shuffle dataset
dataset = dataset.sample(frac=1)
#-1 to include all possible columns.
target = dataset.iloc[:,-1].values
data = dataset.iloc[:,:-1].values
data = data/255.0
#This is a sequential model
machine = Sequential()
#First layer needs to input
#Last layer needs to predict
# Dense defines the type of layer: Each node is connected
# We pick 512 arbitrarially as a starting number of features. There's going to be trial and errors
# Sigmoid means the sigmoid activation function: digits are between 0 and 1.
# Data.shape gets the number of pixels from the data: 784
# activation function "relu" is a substitute for sigmoid.
# relu is non-linear, accepts inputs from negative infinity to positive infinity, isn't bounded to f(x)=1 so it obtains more information f(x) -> infinity
machine.add(layers.Dense(512,
activation="sigmoid",
input_shape=(data.shape[1],)
))
# Second layer, 128 features
machine.add(layers.Dense(128,
activation="sigmoid"))
#Third layer, 64 features
machine.add(layers.Dense(64,
activation="sigmoid"))
# Final layer, our images could be 0-9, so 10
machine.add(layers.Dense(10,
activation="softmax"))
# We need to inject an optimizer to turn this into a machine.
# Sgd, stochastic gradient descent.
# Sparse categorical cross entropy
# Accuracy, backwards propogation:
# we check the outcome with our dataset.
# We use this to adjust our "beta" later on to improve the accuracy
machine.compile(optimizer="sgd",
loss="sparse_categorical_crossentropy",
metrics=['accuracy'])
machine.fit(data, target, epochs=90, batch_size=64)
#Batch_size: Consult 64 pictures out of the 42 thousand of pictures we have.
#Epochs: how many rounds of backward propogation until you stop.
#Rule, these two numbers multiplied together should be bigger than your data size.
# Accuracy: 0.9187
# The accuracy score talking about the best fitting model
# When we do validation, the validation accuracy could be small since we picked 64 pictures.
# New_target is the prediction
# Argmax, we're going to get a bunch of probabilities, we are still going to want the maximum probability.
new_target = numpy.argmax(machine.predict(new_data), axis=-1)
#Simulating real world with new data
new_data = pandas.read_csv("new_data.csv")
filename_list = new_data.iloc[:,-1].values
new_data = new_data.iloc[:,:-1].values
new_data = new_data/255.0
prediction = numpy.argmax(machine.predict(new_data), axis = -1)
result = pandas.DataFrame()
result['filename'] = filename_list
result['prediction'] = predict
print(results)
# import keras
# from keras.models import Sequential
# from keras import layers
# import pandas
# import numpy
# dataset = pandas.read_csv("dataset.csv")
# dataset = dataset.sample(frac=1)
# target = dataset.iloc[:,-1].values
# data = dataset.iloc[:,:-1].values
# data = data/255.0
# machine = Sequential()
# machine.add(layers.Dense(512,
# activation="relu",
# input_shape=(data.shape[1],)
# ))
# machine.add(layers.Dense(128,
# activation="relu"))
# machine.add(layers.Dense(64,
# activation="relu"))
# machine.add(layers.Dense(10, activation="softmax"))
# machine.compile(optimizer="sgd",
# loss="sparse_categorical_crossentropy",
# metrics=['accuracy'])
# machine.fit(data, target, epochs=30, batch_size=64)
# new_data = pandas.read_csv("new_data.csv")
# filename_list = new_data.iloc[:,-1].values
# new_data = new_data.iloc[:,:-1].values
# new_data = new_data/255.0
# prediction = numpy.argmax(machine.predict(new_data), axis=-1)
# result = pandas.DataFrame()
# result['filename'] = filename_list
# result['prediction'] = prediction
# print(result)