def train_model(dataset, h0_dim, h1_dim, out_dim): X_train, y_train, X_test, y_test = dataset batch_size = 128 nb_epoch = 100 model = Sequential() model.add( RNN(h0_dim, input_shape=(None, X_train.shape[-1]), return_sequences=True)) model.add(TimeDistributedDense(out_dim)) model.add(Activation("linear")) model.compile(loss="mse", optimizer="rmsprop") #model.get_config(verbose=1) #yaml_string = model.to_yaml() #with open('ifshort_mlp.yaml', 'w') as f: # f.write(yaml_string) early_stopping = EarlyStopping(monitor='val_loss', patience=10) checkpointer = ModelCheckpoint(filepath="/tmp/ifshort_rnn_weights.hdf5", verbose=1, save_best_only=True) model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=2, validation_data=(X_test, y_test), callbacks=[early_stopping, checkpointer])
def train_model(dataset, h0_dim, h1_dim, y_dim): X_train, y_train, X_test, y_test = dataset batch_size = 512 nb_epoch = 100 model = Sequential() model.add(Dense(h0_dim, input_shape=(X_train.shape[1],), init='uniform', W_regularizer=l2(0.0005), activation='relu')) model.add(Dense(h1_dim, init='uniform', W_regularizer=l2(0.0005), activation='relu')) model.add(Dense(y_dim, init='uniform', W_regularizer=l2(0.0005))) rms = RMSprop() sgd = SGD(lr=0.01, decay=1e-4, momentum=0.6, nesterov=False) model.compile(loss='mse', optimizer=sgd) #model.get_config(verbose=1) #yaml_string = model.to_yaml() #with open('ifshort_mlp.yaml', 'w') as f: # f.write(yaml_string) early_stopping = EarlyStopping(monitor='val_loss', patience=10) checkpointer = ModelCheckpoint(filepath="/tmp/ifshort_mlp_weights.hdf5", verbose=1, save_best_only=True) model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=2, validation_data=(X_test, y_test), callbacks=[early_stopping, checkpointer])
def train_model(dataset, h0_dim, h1_dim, out_dim): X_train, y_train, X_test, y_test = dataset batch_size = 128 nb_epoch = 100 model = Sequential() model.add(RNN(h0_dim, input_shape=(None, X_train.shape[-1]), return_sequences=True)) model.add(TimeDistributedDense(out_dim)) model.add(Activation("linear")) model.compile(loss="mse", optimizer="rmsprop") #model.get_config(verbose=1) #yaml_string = model.to_yaml() #with open('ifshort_mlp.yaml', 'w') as f: # f.write(yaml_string) early_stopping = EarlyStopping(monitor='val_loss', patience=10) checkpointer = ModelCheckpoint(filepath="/tmp/ifshort_rnn_weights.hdf5", verbose=1, save_best_only=True) model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=2, validation_data=(X_test, y_test), callbacks=[early_stopping, checkpointer])
activation='relu')) #model.add(Dropout(0.2)) model.add(Dense(40, init='uniform', # init=lambda shape: uniform(shape, scale=0.05), W_regularizer=l2(0.0005), activation='relu')) #model.add(Dropout(0.2)) model.add(Dense(1, init='uniform', # init=lambda shape: uniform(shape, scale=0.005), W_regularizer=l2(0.0005))) rms = RMSprop() sgd = SGD(lr=0.01, decay=1e-4, momentum=0.6, nesterov=False) model.compile(loss='mse', optimizer=sgd) #model.get_config(verbose=1) yaml_string = model.to_yaml() with open('ifshort_mlp.yaml', 'w') as f: f.write(yaml_string) early_stopping = EarlyStopping(monitor='val_loss', patience=10) checkpointer = ModelCheckpoint(filepath="/tmp/ifshort_mlp_weights.hdf5", verbose=1, save_best_only=True) model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=2, validation_data=(X_test, y_test), callbacks=[early_stopping, checkpointer])
W_regularizer=l2(0.0005), activation='relu')) #model.add(Dropout(0.2)) model.add( Dense( 1, init='uniform', # init=lambda shape: uniform(shape, scale=0.005), W_regularizer=l2(0.0005))) rms = RMSprop() sgd = SGD(lr=0.01, decay=1e-4, momentum=0.6, nesterov=False) model.compile(loss='mse', optimizer=sgd) #model.get_config(verbose=1) yaml_string = model.to_yaml() with open('ifshort_mlp.yaml', 'w') as f: f.write(yaml_string) early_stopping = EarlyStopping(monitor='val_loss', patience=10) checkpointer = ModelCheckpoint(filepath="/tmp/ifshort_mlp_weights.hdf5", verbose=1, save_best_only=True) model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=2, validation_data=(X_test, y_test), callbacks=[early_stopping, checkpointer])