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72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
5 years ago
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"""
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In this example we are trying to predict color of a point only with the x and y values.
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x,y,color
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x : X coordinate on a cartisian coordinate plain
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y : Y coordinate on a cartision coordinate plain
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color: One of two values (0 , 1).
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"""
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import tensorflow as tf
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from tensorflow import keras
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import pandas as pd
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import numpy as np
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train_df = pd.read_csv('./data/train.csv')
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# You always want to shuffle your training data. This can avoid things like in this particular
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# Set of training data, where our colors are all grouped in sequence. We want to avoid having
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# Highly correalated data next to each other. Much more like how we will see in the wild.
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# This shuffle method works in place. You do not need to set the result to another variable.
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np.random.shuffle(train_df.values)
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print(train_df.head())
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# Sequential here lets us define our layers in sequence from input to output.
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# This example is using a fully connected feed forward network (Dense).
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# 2 Input -> 4 Hidden -> 2 output
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model = keras.Sequential([
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# Our input data is x and y
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keras.layers.Dense(4, input_shape=(2,), activation='relu'),
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keras.layers.Dense(2, activation='sigmoid')])
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# You can view what a loss function here is https://www.tensorflow.org/api_docs/python/tf
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# CategoricalCrossentropy is encoded in a "One hot" fashion. SparseCategoricalCrossentropy
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# Expects two or more labels to be encoded as integers.
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# Don't know what Logits means? https://stackoverflow.com/questions/34240703/what-is-logits-softmax-and-softmax-cross-entropy-with-logits
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# So we aren't using input values between 0 and 1 so we want to use the keyword.
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model.compile(optimizer='adam',
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loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy'])
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print(train_df.head())
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# Converts dataframe to a numpy array stacking the two input values together
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x = np.column_stack((train_df.x.values, train_df.y.values))
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# TUNING ###################
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# Look up what the arg values are expected or supported. Here they are using a np array but there
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# Are other acceptable values.
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# model.fit(x, train_df.color.values, batch_size=16) # 50-ish %
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# model.fit(x, train_df.color.values, batch_size=4) # 81-ish %
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# Try going back and doubling (8) the the hidden layer size. I got 88% accuracy.
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# Running 5 epochs showed that the NN can classify 100% of the training data correctly
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# This makes sense because our data is linear and well labeled.
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model.fit(x, train_df.color.values, batch_size=4, epochs=5)
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test_df = pd.read_csv('./data/test.csv')
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test_x = np.column_stack((test_df.x.values, test_df.y.values))
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print("EVALUATION")
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# Is this a "manual" holdout?
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model.evaluate(test_x, test_df.color.values)
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