73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
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# coding: utf-8
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# In[1]:
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# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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# text in Western (Windows 1252)
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import numpy as np
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# import StringIO
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import math
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Merge
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from keras.layers.merge import concatenate
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from keras import regularizers
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from keras.layers.convolutional import Conv1D
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from keras.layers.convolutional import MaxPooling1D
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from keras.constraints import maxnorm
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from keras.layers import Flatten
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from keras.optimizers import SGD
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from keras.models import load_model
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np.random.seed(7)
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# get_ipython().magic('run ../../../prepare_data.py')
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# import sys
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# # sys.path.insert(0, '../../../')
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# sys.path.insert(0, '/home/luka/Developement/accetuation/')
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from prepare_data import *
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# X_train, X_other_features_train, y_train, X_validate, X_other_features_validate, y_validate = generate_full_matrix_inputs()
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# save_inputs('../../internal_representations/inputs/shuffeled_matrix_train_inputs_other_features_output_11.h5', X_train, y_train, other_features = X_other_features_train)
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# save_inputs('../../internal_representations/inputs/shuffeled_matrix_validate_inputs_other_features_output_11.h5', X_validate, y_validate, other_features = X_other_features_validate)
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X_train, X_other_features_train, y_train = load_inputs('cnn/internal_representations/inputs/shuffeled_matrix_train_inputs_other_features_output_11.h5', other_features=True)
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X_validate, X_other_features_validate, y_validate = load_inputs('cnn/internal_representations/inputs/shuffeled_matrix_validate_inputs_other_features_output_11.h5', other_features=True)
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num_examples = len(X_train) # training set size
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nn_output_dim = 11
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nn_hdim = 516
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word_processor = Sequential()
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word_processor.add(Conv1D(43, (3), input_shape=(23, 43), padding='same', activation='relu'))
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word_processor.add(Conv1D(43, (3), padding='same', activation='relu'))
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word_processor.add(MaxPooling1D(pool_size=2))
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word_processor.add(Flatten())
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word_processor.add(Dense(516, activation='relu', kernel_constraint=maxnorm(3)))
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metadata_processor = Sequential()
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metadata_processor.add(Dense(256, input_dim=167, activation='relu'))
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model = Sequential()
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model.add(Merge([word_processor, metadata_processor], mode='concat')) # Merge is your sensor fusion buddy
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model.add(Dense(1024, input_dim=(516 + 256), activation='relu'))
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model.add(Dropout(0.3))
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model.add(Dense(1024, input_dim=(516 + 256), activation='relu'))
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model.add(Dropout(0.2))
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model.add(Dense(nn_output_dim, activation='sigmoid'))
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# In[10]:
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# epochs = 5
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# lrate = 0.1
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# decay = lrate/epochs
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# sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
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model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
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model.fit([X_train, X_other_features_train], y_train, validation_data=([X_validate, X_other_features_validate], y_validate), epochs=10, batch_size=10)
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model.save('v1_1.h5')
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