illiumst
11fc99cbb5
Function for loading colored routes from a file. This way, a user can define his own paths without a manual coordinate input.
91 lines
4.0 KiB
Python
91 lines
4.0 KiB
Python
from keras.layers import TimeDistributed, Dense, LSTM, MaxPooling1D, Convolution1D, UpSampling2D, RepeatVector, \
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MaxPooling2D, Convolution2D, Deconvolution2D, ConvLSTM2D, AveragePooling3D, Flatten, Reshape, ZeroPadding2D, \
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ZeroPadding1D, Dropout
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from keras.models import Sequential
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from keras.callbacks import TensorBoard
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from keras import backend as K
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from temporalPooling import TemporalMaxPooling
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from keras.utils import plot_model
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import time
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import numpy as np
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import pickle
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from math import sqrt
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# from tools import TrackCollection
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def get_batch(X, size):
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a = np.random.choice(len(X), size, replace=False)
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return X[a]
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def load_preprocesseed_data(filename):
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if not filename.endswith('.pik'):
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raise TypeError('input File needs to be a Pickle object ".pik"!')
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with open(filename, 'rb') as f:
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data = pickle.load(f)
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return data
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# Autoencoder From:
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# https://github.com/BinRoot/TensorFlow-Book/blob/master/ch07_autoencoder/Concept01_autoencoder.ipynb
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if __name__ == '__main__':
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'''HERE IS THE TRAINING!!!!!'''
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trackCollection = load_preprocesseed_data('test_track.pik')
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dataArray = trackCollection.as_n_sample_4D(5).astype(int)
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# [nb_samples, nb_frames, width, height, channels] # if using dim_ordering = 'tensorflow'
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# [nb_samples, nb_frames, channels, width, height] # if using dim_ordering = 'theano'
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dataArray = dataArray[..., None]
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samples, timestep, w, h, c = dataArray.shape
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classes = 10
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K.set_image_dim_ordering('tf')
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seq = Sequential()
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seq.add(TimeDistributed(Convolution2D(activation='relu', filters=classes, kernel_size=(3, 3), strides=1), input_shape=(timestep, w, h, c)))
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seq.add(TimeDistributed(MaxPooling2D(pool_size=2, strides=2)))
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seq.add(TimeDistributed(Convolution2D(activation='relu', filters=classes, kernel_size=(3, 3), strides=1)))
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seq.add(TimeDistributed(MaxPooling2D(pool_size=2, strides=2)))
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seq.summary()
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time.sleep(1)
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seq.add(TimeDistributed(Convolution2D(activation='relu', filters=classes, kernel_size=(3, 3), strides=1)))
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# seq.add(TimeDistributed(Dropout(0.2)))
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seq.add(TimeDistributed(MaxPooling2D(pool_size=2, strides=2)))
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seq.add(TimeDistributed(Flatten()))
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seq.add(LSTM(int(seq.layers[-1].output_shape[-1]), return_sequences=False)) # ,recurrent_dropout=0.2, dropout=0.2))
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seq.add(Dense(int(seq.layers[-1].output_shape[-1]), activation=None))
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seq.add(Dense(classes, activation='softmax'))
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seq.add(Dense(int(seq.layers[-2].output_shape[-1]), activation=None))
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# seq.summary()
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# time.sleep(1)
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seq.add(RepeatVector(timestep))
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seq.add(LSTM(seq.layers[-1].output_shape[-1], return_sequences=True)) # , recurrent_dropout=0.2, dropout=0.2))
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reValue = int(sqrt(seq.layers[-1].output_shape[-1]//classes))
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seq.add(TimeDistributed(Reshape((reValue, reValue, classes))))
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seq.add(TimeDistributed(UpSampling2D(2)))
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# seq.add(TimeDistributed(Dropout(0.2)))
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seq.add(TimeDistributed(Deconvolution2D(activation='relu', filters=classes, kernel_size=(3,3), strides=1)))
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seq.add(TimeDistributed(UpSampling2D(2)))
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seq.add(TimeDistributed(Deconvolution2D(activation='relu', filters=classes//2, kernel_size=(3,3), strides=1)))
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seq.add(TimeDistributed(UpSampling2D(2)))
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seq.add(TimeDistributed(Deconvolution2D(activation='relu', filters=1, kernel_size=(3,3), strides=1)))
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seq.compile(loss='binary_crossentropy', optimizer='adagrad') # adadelta
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seq.summary()
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time.sleep(1)
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bCallBack = TensorBoard(log_dir='./logdir', histogram_freq=10, write_graph=True, write_images=True)
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seq.fit(dataArray, dataArray, batch_size=500, epochs=100, callbacks=[bCallBack])
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# reconstructed = seq.predict(dataArray)
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# seq.save('TDConv_LSTM_D_LSTM_TDConv')
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# tc2 = load_preprocesseed_data('test_track.pik')
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# testArrays = tc2.as_n_sample_4D(5)
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# testArrays = testArrays[..., None]
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# get_3rd_layer_output = K.function([seq.layers[0].input],
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# [seq.layers[3].output])
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# layer_output = get_3rd_layer_output([])[0]
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