from sklearn.svm import LinearSVC, SVC from sklearn.metrics import accuracy_score from sklearn.neighbors import KNeighborsClassifier import numpy as np x_train = np.array([[0, 0], [1, 0], [0, 1], [1, 1]]) y_train = np.array([0, 1, 1, 0]) print(x_train.shape, y_train.shape) model = LinearSVC() model = SVC(kernel='poly', degree=2, gamma=1, coef0=0) model = SVC(kernel='rbf', degree=2, gamma=1, coef0=0) model = SVC(kernel='sigmoid', degree=2, gamma=1, coef0=0) model = KNeighborsClassifier(n_neighbors=1) from keras.models import Sequential, Model from keras.layers import Dense, LSTM, Input model = Sequential() model.add(Dense(16, activation='sigmoid', input_shape=(2, ))) model.add(Dense(1)) model.summary() # quit() model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc']) model.fit(x_train, y_train) y_pred = model.predict(x_train) print(y_pred) # print(accuracy_score(y_train, y_pred))
# In[58]: X_train.shape # https://keras.io/getting-started/sequential-model-guide/ # # https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense # # In[59]: model = Sequential() # In[60]: model.add(layers.Dense(10, input_dim=input_dim, activation='relu')) # 618 (input_dim) parameters, with 10 neurons in the first hidden layer (output) # In[61]: model.add(layers.Dense(1, activation='sigmoid')) # In[62]: # Start training - configuraton of the lerning process: # In[63]: model.compile(loss='binary_crossentropy', optimizer='adam',