fname_model = "pickled_files/models/mlp_regression_" + str( serial_num) + ".pkl" mlpr.dump(fname_model) hash_model = model_db.find_hash(fname_model) model_db.store_cur_data([hash_model], columns=['model_hash']) # Store the data that trained the model data.dump_X(serial_num=serial_num) data.dump_Y(serial_num=serial_num) # Store all the data in the data db and dump the db model_db.store_data(serial_num) model_db.dump() # Analyze the model mlpr_acc = mlpr.evaluate() print(mlpr_acc) # Make predictions using the testing set pred_y = mlpr.predict(data.test_x) # The coefficients # print('Coefficients: \n', mlpr.model.coefs_) # The mean squared error print("Mean squared error: %.2f" % mean_squared_error(data.test_y, pred_y)) # Explained variance score: 1 is perfect prediction print('Variance score: %.2f' % r2_score(data.test_y, pred_y)) # Plot outputs x = [] for i, feat_vec in enumerate(data.test_x):
#create model print('creating model') model = Sequential() model.add(Dense(100, input_dim=1, init='uniform', activation='relu')) model.add(Dense(24, init='uniform', activation='sigmoid')) model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mean_squared_error']) # training print('Training') model.fit(X, load_dh, batch_size=10, nb_epoch=10000, verbose=2, validation_split=0.3, shuffle=True) scores = model.evaluate(X, load_dh) print("%s: %.2f%%" % (model.metrics_names[1], scores[1])) # Multilayer Perceptron to Predict International Airline Passengers (t+1, given t, t-1, t-2) import numpy import matplotlib.pyplot as plt import pandas import math from keras.models import Sequential from keras.layers import Dense # convert an array of values into a dataset matrix def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1):
# Initializing the model model = Sequential() model.add(Dense(150, input_dim=13, activation="relu")) model.add(Dense(200, activation="tanh")) model.add(Dense(120, activation="tanh")) model.add(Dense(200, activation="tanh")) model.add(Dense(num_of_classes, activation="softmax")) model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"]) return model # building a cnn model using train data set and validating on test data set model = design_mlp() # fitting model on train data model.fit(x=x_train, y=y_train, batch_size=500, epochs=5) # Evaluating the model on test data eval_score_test = model.evaluate(x_test, y_test, verbose=1) # accuracy on test data set print("Accuracy: %.3f%%" % (eval_score_test[1] * 100)) # Evaluating the model on train data eval_score_train = model.evaluate(x_train, y_train, verbose=0) # accuracy on train data set print("Accuracy: %.3f%%" % (eval_score_train[1] * 100)) #######################################END###################################
train_columns = ['season', 'month', 'hour', 'holiday', 'weekday', 'workingday', 'weather', 'temp', 'humidity'] X = df_train[[x for x in all_columns if x.startswith(tuple(train_columns))]] # getting all desired # print(X) X = X.drop(columns=['weather_4']) print(X.columns) y = df_train['count'] # Creating the split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # Initializing MLPRegressor neural = MLPRegressor(hidden_layer_sizes=(100, 60, 40, 20), activation='relu', solver='lbfgs', alpha=0.0001, verbose=True) neural.fit(X_train, y_train) _, test_acc = neural.evaluate(X_test, y_test, verbose=0) # evaluating MLPRegressor print('Test: %.3f' % test_acc) # Initializing Sequential NN model = sequential_nn_model(X_train, y_train) df_test['weather_4'] = 0 df_test = df_test[[x for x in all_columns if x.startswith(tuple(train_columns))]] df_test = df_test.drop(columns=['weather_4']) print(df_test.columns) test_array = df_test.to_numpy() predictions = model.predict(test_array) individual_predictions = [transform_list_item(x) for x in predictions] for i, y in enumerate(individual_predictions): if individual_predictions[i] < 0: individual_predictions[i] = 0