si_lab05

import numpy as np
from matplotlib import pyplot as plt
from matplotlib import rcParams
import pandas as pd
# rcParams['font.family'] = 'Times New Roman'
# rcParams['font.size'] = 16
# np.set_printoptions(precision=2)
x = np.arange(0,1,0.01)
y = x.copy()
X,Y = np.meshgrid(x,y)
wx = 0.1
wy = 0.3
S = wx*X+wy*Y
out = S>0.15
fig, ax = plt.subplots(1,1)
ax.imshow(out)
ticks = np.around(np.arange(-0.2,1.1,0.2), 3)
ax.set_xticklabels(ticks)
ax.set_yticklabels(ticks)
plt.gca().invert_yaxis()

from sklearn.datasets import load_iris
data = load_iris()
y = data.target
X = data.data
y = pd.Categorical(y)
y = pd.get_dummies(y).values
class_num = y.shape[1]

from keras.models import Sequential
from keras.layers import Input, Dense
from keras.optimizers import Adam, RMSprop, SGD
from keras.utils import plot_model
class_num = 3
model = Sequential()
model.add(Dense(64, input_shape = (X.shape[1],), activation = 'relu'))
# model.add(Dense(64, activation = 'relu'))
# model.add(Dense(64, activation = 'relu'))
model.add(Dense(class_num, activation = 'softmax'))
learning_rate = 0.001
model.compile(optimizer= Adam(learning_rate), loss='categorical_crossentropy', metrics=('accuracy'))
model.summary()
plot_model(model,to_file="my_model.png")

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import *
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
model.fit(X_train, y_train, batch_size=32, epochs=100, validation_data=(X_test, y_test), verbose=2)

from matplotlib import pyplot as plt
historia = model.history.history
floss_train = historia['loss']
floss_test = historia['val_loss']
acc_train = historia['accuracy']
acc_test = historia['val_accuracy']
fig,ax = plt.subplots(1,2, figsize=(20,10))
epochs = np.arange(0, 100)
ax[0].plot(epochs, floss_train, label = 'floss_train')
ax[0].plot(epochs, floss_test, label = 'floss_test')
ax[0].set_title('Funkcje strat')
ax[0].legend()
ax[1].set_title('Dokladnosci')
ax[1].plot(epochs, acc_train, label = 'acc_train')
ax[1].plot(epochs, acc_test, label = 'acc_test')
ax[1].legend()


# from sklearn.model_selection import KFold
# from sklearn.metrics import accuracy_score
# X_train, X_test, y_train, y_test = train_test_split(X,y,
#  test_size=0.2)
# accs = []
# scaler = StandardScaler()
# for train_index, test_index in KFold(5).split(X_train):
#     X_train_cv = X_train[train_index,:]
#     X_test_cv = X_train[test_index,:]
#     y_train_cv = y_train[train_index,:]
# y_test_cv = y_train[test_index,:]
# X_train_cv = scaler.fit_transform(X_train_cv)
# X_test_cv = scaler.transform(X_test_cv)
# model.fit(X_train_cv, y_train_cv, batch_size=32,
#  epochs=100, validation_data=
#  (X_test_cv,y_test_cv), verbose=2)
# y_pred = model.predict(X_test_cv).argmax(axis=1)
# y_test_cv = y_test_cv.argmax(axis=1)