user_embedding = Embedding(n_users, n_latent_factors, embeddings_regularizer=regularizers.l2(0.00001), name='user_embedding')(user_input) """- 벡터화(Flatten)""" # Item latent vector movie_vec = Flatten()(movie_embedding) # User latent vector user_vec = Flatten()(user_embedding) """- 모델링(Modeling)""" r_hat = dot([movie_vec, user_vec], axes=-1) model = Model([user_input, movie_input], r_hat) model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy']) """- 모델 훈련(Train Model)""" # Commented out IPython magic to ensure Python compatibility. hist = model.fit([train_df.userId, train_df.movieId], train_df.rating, validation_split=0.1, batch_size=128, epochs=50, verbose=1) print(hist.history.keys()) print('train loss: ', hist.history['loss'][-1]) print('train acc: ', hist.history['accuracy'][-1]) print('val acc: ', hist.history['val_loss'][-1]) print('val acc: ', hist.history['val_accuracy'][-1])
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)) # In[ ]:
model = tf.keras.models.Sequential([ tf.keras.layers.Input(shape=(5)), tf.keras.layers.Dense(12800, activation="relu"), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(1600, activation="relu"), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(80, activation="relu"), tf.keras.layers.Dense(80, activation="relu"), tf.keras.layers.Dense(80, activation="relu"), tf.keras.layers.Dense(80, activation="relu"), tf.keras.layers.Dense(80, activation="relu"), tf.keras.layers.Dense(80, activation="relu"), tf.keras.layers.Dense(80, activation="relu"), tf.keras.layers.Dense(4) ]) model.compile(optimizer="adam", loss="mse", metrics=['mae', 'mse']) model.fit(x_train, y_train, epochs=50) mse, mae, mse = model.evaluate(x_test, y_test, verbose=2) print('\nTest mean absolute error:', mae)
model.add(BatchNormalization(axis=-1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2,2))) # model.add(Flatten()) # ## Fully connected layer model.add(Dense(512)) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(Dropout(0.2)) model.add(Dense(6)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy']) model.fit(X_train,label_train, steps_per_epoch=1, validation_data=(x_val,y_val), validation_steps=1, epochs=5, verbose=2) model.predict_generator(test_batch,verbose=2) train_batch.filenames