56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
# This is a sample Python script.
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# Press Shift+F10 to execute it or replace it with your code.
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# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.optimizers import Adam
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import pandas as pd
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import tensorflow as tf
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import numpy as np
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from sklearn.preprocessing import LabelEncoder
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def get_x_y(data):
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lb = LabelEncoder()
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data = data.drop(["Location 1"], axis=1)
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data = data.drop(columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post", "CrimeDate", "Inside/Outside"], axis=1)
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for column_name in data.columns:
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data[column_name] = lb.fit_transform(data[column_name])
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x = data.drop('Weapon', axis=1)
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y = data['Weapon']
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return data, x, y
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def train_model():
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train = pd.read_csv('baltimore_train.csv')
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data_train, x_train, y_train = get_x_y(train)
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normalizer = tf.keras.layers.Normalization(axis=1)
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normalizer.adapt(np.array(x_train))
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model = Sequential(normalizer)
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model.add(Dense(64, activation="relu"))
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model.add(Dense(10, activation='relu'))
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model.add(Dense(10, activation='relu'))
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model.add(Dense(10, activation='relu'))
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model.add(Dense(5, activation="softmax"))
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model.compile(Adam(learning_rate=0.01), loss='sparse_categorical_crossentropy', metrics = ['accuracy'] )
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model.summary()
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history = model.fit(
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x_train,
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y_train,
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epochs=20,
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validation_split=0.2)
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hist = pd.DataFrame(history.history)
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hist['epoch'] = history.epoch
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model.save('baltimore_model3')
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train_model()
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