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82 lines
3.0 KiB
82 lines
3.0 KiB
#!/usr/bin/env python
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# coding: utf-8
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# This is the code what she teaches in the first hour in https://www.youtube.com/watch?v=qFJeN9V1ZsI
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import numpy as np
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from random import randint
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from sklearn.utils import shuffle
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from sklearn.preprocessing import MinMaxScaler
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train_labels = []
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train_samples = []
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for i in range(50):
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train_samples.append(randint(13, 64))
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train_labels.append(1)
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train_samples.append(randint(65, 100))
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train_labels.append(0)
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for i in range(1000):
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train_samples.append(randint(13, 64))
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train_labels.append(0)
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train_samples.append(randint(65, 100))
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train_labels.append(1)
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train_labels = np.array(train_labels)
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train_samples = np.array(train_samples)
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train_labels, train_samples = shuffle(train_labels, train_samples)
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scaler = MinMaxScaler(feature_range=(0,1))
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# `reshape` the data from 1D to fit in the fit() function ... instead of x we need [x]
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# I assume because the array could contain any number of el, but we only have one, so it looks odd
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scaled_train_samples = scaler.fit_transform(train_samples.reshape(-1, 1))
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#print(scaled_train_samples)
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Activation, Dense
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from tensorflow.keras.optimizers import Adam
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from tensorflow.keras.metrics import categorical_crossentropy
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gpus = tf.config.experimental.list_physical_devices('GPU')
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print('GPUs = ', len(gpus))
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#tf.config.experimental.set_memory_growth(gpus[0], True)
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model = Sequential([
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Dense(units=16, input_shape=(1,), activation='relu'),
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Dense(units=32, activation='relu'),
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Dense(units=2, activation='softmax'),
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])
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#model.summary()
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model.compile(optimizer=Adam(learning_rate=0.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
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#model.fit(x=scaled_train_samples, y=train_labels, batch_size=10, epochs=30, shuffle=True, verbose=2)
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model.fit(x=scaled_train_samples, y=train_labels, validation_split=0.1, batch_size=10, epochs=30, shuffle=True, verbose=2)
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test_labels = []
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test_samples = []
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for i in range(10):
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test_samples.append(randint(13, 64))
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test_labels.append(1)
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test_samples.append(randint(65, 100))
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test_labels.append(0)
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for i in range(200):
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test_samples.append(randint(13, 64))
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test_labels.append(0)
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test_samples.append(randint(65, 100))
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test_labels.append(1)
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test_labels = np.array(test_labels)
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test_samples = np.array(test_samples)
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test_labels, test_samples = shuffle(test_labels, test_samples)
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scaled_test_samples = scaler.fit_transform(test_samples.reshape(-1, 1))
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# # Predict
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predictions = model.predict(x=scaled_test_samples, batch_size=10, verbose=0)
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print(predictions)
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# seems to work ONLY in jupyter
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from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
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rounded_predictions = np.argmax(predictions, axis=-1)
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cm = confusion_matrix(y_true=test_labels, y_pred=rounded_predictions)
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cm_plot_labels = ['no_side_fx', 'has_side_fx']
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disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=cm_plot_labels)
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disp.plot()
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