# In[25]: from sklearn.linear_model import SGDClassifier clf = SGDClassifier(loss="hinge", penalty="l2", max_iter=50) clf.fit(X_train, y_train) clf.score(X_test, y_test) # In[ ]: from keras.models import Sequential from keras import layers input_dim = X_train.shape[1] # Number of features model = Sequential() model.add(layers.Dense(10, input_dim=input_dim, activation='relu')) model.add(layers.Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary() history = model.fit(X_train, y_train, epochs=100, verbose=False, validation_data=(X_test, y_test), batch_size=10) loss, accuracy = model.evaluate(X_train, y_train, verbose=False) print("Training Accuracy: {:.4f}".format(accuracy)) loss, accuracy = model.evaluate(X_test, y_test, verbose=False) print("Testing Accuracy: {:.4f}".format(accuracy))
model = NMF(n_components=6, init='random', random_state=0,tol = 5e-3) W = model.fit_transform(img_train_f) H = model.components_ matrix = np.dot(W,H) matrix.shape X_train = matrix X_train.resize(1530,224,224,1) X_train.shape from keras.layers.normalization import BatchNormalization model = Sequential() model.add(Conv2D(32, (3, 3), input_shape=(224,224,1))) model.add(BatchNormalization(axis=-1)) model.add(Activation('relu')) model.add(Conv2D(32, (3, 3))) model.add(BatchNormalization(axis=-1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2))) # model.add(Conv2D(64,(3, 3))) model.add(BatchNormalization(axis=-1)) model.add(Activation('relu')) model.add(Conv2D(64, (3, 3))) model.add(BatchNormalization(axis=-1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2))) #