68 lines
No EOL
2.1 KiB
Python
68 lines
No EOL
2.1 KiB
Python
#Import libaries and datasets
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import matplotlib.pyplot as plt
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import numpy as np
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import tensorflow as tf
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import pandas as pd
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import seaborn as sns
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import pickle
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import random
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import os
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import numpy as np
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from keras.utils import np_utils
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with open(os.path.join("dataset", "train.p"), mode='rb') as training_data:
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train = pickle.load(training_data)
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with open(os.path.join("dataset", "valid.p"), mode='rb') as validation_data:
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valid = pickle.load(validation_data)
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X_train, y_train = train['features'], train['labels']
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X_valid, y_valid = valid['features'], valid['labels']
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from sklearn.utils import shuffle
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X_train, y_train = shuffle(X_train, y_train)
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X_valid, y_valid = shuffle(X_valid, y_valid)
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# Normalize image to [0, 1]
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X_train_norm = X_train / 255
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X_valid_norm = X_valid / 255
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#Wähle Klassen 0-19
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mask = np.isin(y_train, range(20))
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X_train_subset = X_train_norm[mask]
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y_train_subset = y_train[mask]
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mask = np.isin(y_valid, range(20))
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X_valid_subset = X_valid_norm[mask]
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y_valid_subset = y_valid[mask]
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num_classes = 20 # Anzahl der Straßenschilder-Klassen
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y_train_subset = np_utils.to_categorical(y_train_subset, num_classes)
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y_valid_subset = np_utils.to_categorical(y_valid_subset, num_classes)
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from tensorflow.keras import datasets, layers, models
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model = models.Sequential()
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# TODO: Add layers to the model:
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model.add(layers.Flatten(input_shape=(32, 32, 3))) # Eingabeschicht, Bildgröße: 32x32, 3 Kanäle (RGB)
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model.add(layers.Dense(128, activation='relu')) # Versteckte Schicht mit 128 Neuronen und ReLU-Aktivierung
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model.add(layers.Dense(20, activation='softmax')) # Ausgabeschicht mit Anzahl der Klassen und Softmax-Aktivierung
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# Prints a summary of your network
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model.summary()
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model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy', metrics = ['accuracy'])
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# TODO: Choose the batch size and the epochs
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history = model.fit(x = X_train_subset,
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y = y_train_subset,
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batch_size = 32,
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epochs = 500,
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verbose = 1,
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validation_data = (X_valid_subset, y_valid_subset))
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model.save('saved_model/fullyConnectedNeuralNetwork.h5') |