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feat: add plot func in order to compare validation

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on three sessions after testing on one
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Skudalen 2021-07-20 11:53:47 +02:00
parent 14d9b65060
commit cb7da4c657
1 changed files with 59 additions and 42 deletions
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101
Neural_Network_Analysis.py
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@ -12,6 +12,7 @@ from keras.callbacks import Callback, CSVLogger, ModelCheckpoint
from pathlib import Path
import pandas as pd
import matplotlib.pyplot as plt
#from matplotlib.legend import _get_legend_handles_
import statistics
import csv
@ -150,81 +151,97 @@ def plot_4_x_inverse_cross_val(X, y, session_lengths, nr_sessions, batch_size=64
# Ouput: None -> plot
def plot_4_x_average_val(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
history_dict = {'GRU': [],
'LSTM': [],
'FFN': [],
'CNN_1D': []}
history_dict = {'GRU_train': [],
'LSTM_train': [],
'FFN_train': [],
'CNN_1D_train': []}
history_dict_val = {'GRU_val': [],
'LSTM_val': [],
'FFN_val': [],
'CNN_1D_val': []}
for i in range(nr_sessions):
# Prepare data
X_val_session, X_train_session, y_val_session, y_train_session = prepare_datasets_sessions(X, y, session_lengths, i)
# GRU
model_GRU = GRU(input_shape=(1, 208))
GRU_h = train(model_GRU, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs,
X_validation=X_val_session, y_validation=y_val_session)
history_dict['GRU'].append(GRU_h)
history_dict['GRU_train'].append(GRU_h.history['accuracy'])
history_dict_val['GRU_val'].append(GRU_h.history['val_accuracy'])
del model_GRU
K.clear_session()
# LSTM
model_LSTM = LSTM(input_shape=(1, 208))
LSTM_h = train(model_LSTM, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs,
X_validation=X_val_session, y_validation=y_val_session)
history_dict['LSTM'].append(LSTM_h)
history_dict['LSTM_train'].append(LSTM_h.history['accuracy'])
history_dict_val['LSTM_val'].append(LSTM_h.history['val_accuracy'])
del model_LSTM
K.clear_session()
# FFN
model_FFN = FFN(input_shape=(1, 208))
FFN_h = train(model_FFN, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs,
X_validation=X_val_session, y_validation=y_val_session)
history_dict['FFN'].append(FFN_h)
history_dict['FFN_train'].append(FFN_h.history['accuracy'])
history_dict_val['FFN_val'].append(FFN_h.history['val_accuracy'])
del model_FFN
K.clear_session()
# CNN_1D
model_CNN_1D = CNN_1D(input_shape=(208, 1))
X_train_session = np.reshape(X_train_session, (X_train_session.shape[0], 208, 1))
X_val_session = np.reshape(X_val_session, (X_val_session.shape[0], 208, 1))
CNN_1D_h = train(model_CNN_1D, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs,
X_validation=X_val_session, y_validation=y_val_session)
history_dict['CNN_1D'].append(CNN_1D_h)
history_dict['CNN_1D_train'].append(CNN_1D_h.history['accuracy'])
history_dict_val['CNN_1D_val'].append(CNN_1D_h.history['val_accuracy'])
del model_CNN_1D
K.clear_session()
for key, value in history_dict.items():
print(key)
print(value[0].history['val_accuracy'])
# Averaging out session training for each network
for key in history_dict:
val_key = key + '_val'
history_dict[key] = (np.average(x, y, z, c) for x, y, z, c in zip(history_dict[key][0]['val_accuracy'],
history_dict[key][1]['val_accuracy'],
history_dict[key][2]['val_accuracy'],
history_dict[key][3]['val_accuracy']))
train_key = key + '_train'
history_dict[key] = (np.average(x, y, z, c) for x, y, z, c in zip(history_dict[key][0]['accuracy'],
history_dict[key][1]['accuracy'],
history_dict[key][2]['accuracy'],
history_dict[key][3]['accuracy']))
history_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*history_dict[key])))
for key in history_dict_val:
history_dict_val[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*history_dict_val[key])))
fig, axs = plt.subplots(2, sharey=True)
plt.ylim(0, 1)
'''
history_dict = {'GRU_train': [0.5, 0.8],
'LSTM_train': [0.5, 0.9],
'FFN_train': [0.75, 0.8],
'CNN_1D_train': [0.8, 0.95]}
history_dict_val = {'GRU_val': [0.5, 0.8],
'LSTM_val': [0.5, 0.9],
'FFN_val': [0.75, 0.8],
'CNN_1D_val': [0.8, 0.95]}
'''
# Plot:
axs[0].plot(history_dict['GRU-val'])
axs[0].plot(history_dict['LSTM_val'], 'tab:orange')
axs[0].plot(history_dict['FFN_val'], 'tab:green')
axs[0].plot(history_dict['CNN_1D_val'], 'tab:red')
axs[0].set_title('Avarage validation accuracy with cross-session-training')
fig, axs = plt.subplots(2, sharey=True)
plt.ylim(0, 1)
plt.subplots_adjust(hspace=1.0, top=0.85, bottom=0.15, right=0.75)
fig.suptitle('Avarage accuracy with cross-session-training', fontsize=16)
axs[1].plot(history_dict['GRU_train'])
axs[1].plot(history_dict['LSTM_train'], 'tab:orange')
axs[1].plot(history_dict['FFM_train'], 'tab:green')
axs[1].plot(history_dict['CNN_1D_train'], 'tab:red')
axs[1].set_title('Avarage training accuracy with cross-session-training')
axs[0].plot(history_dict['GRU_train'], label='GRU')
axs[0].plot(history_dict['LSTM_train'], 'tab:orange', label='LSTM')
axs[0].plot(history_dict['FFN_train'], 'tab:green', label='FFN')
axs[0].plot(history_dict['CNN_1D_train'], 'tab:red', label='CNN_1D')
axs[0].set_title('Training accuracy')
axs[1].plot(history_dict_val['GRU_val'], label='GRU')
axs[1].plot(history_dict_val['LSTM_val'], 'tab:orange', label='LSTM')
axs[1].plot(history_dict_val['FFN_val'], 'tab:green', label='FFN')
axs[1].plot(history_dict_val['CNN_1D_val'], 'tab:red', label='CNN_1D')
axs[1].set_title('Validation accuracy')
for ax in axs.flat:
ax.set(xlabel='Epochs', ylabel='Accuracy')
plt.legend(bbox_to_anchor=(1.05, 1.5), title='Networks', loc='center left')
plt.show()
@ -697,6 +714,6 @@ if __name__ == "__main__":
# ----- PLOTTING ------
#plot_4xinverse_cross_val(X, y, session_lengths, NR_SESSIONS, epochs=30)
plot_4_x_average_val(X, y, session_lengths, NR_SESSIONS, epochs=2)
plot_4_x_average_val(X, y, session_lengths, NR_SESSIONS, epochs=30)