Ejemplo n.º 1
0
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])
Ejemplo n.º 2
0
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])
Ejemplo n.º 3
0
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])
Ejemplo n.º 4
0
                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])
Ejemplo n.º 5
0
        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])