Python models.Sequential类代码示例

 def conv2d_work(input_dim):
     seq = Sequential()
     assert self.config['num_conv2d_layers'] > 0
     for i in range(self.config['num_conv2d_layers']):
         seq.add(Conv2D(filters=self.config['2d_kernel_counts'][i], kernel_size=self.config['2d_kernel_sizes'][i], padding='same', activation='relu'))
         seq.add(MaxPooling2D(pool_size=(self.config['2d_mpool_sizes'][i][0], self.config['2d_mpool_sizes'][i][1])))
     return seq

def train_model():
    # (X_train, Y_train, X_test, Y_test) = prapare_train()
    X_ = []
    with open('../data/train_matrix.out') as train_file:
        X_train = json.load(train_file)
        for x in X_train:
            a = len(x)
            print a/2
            x1 = x[:a/2]
            x2 = x[a/2:]
            x3 = []
            x3.append(x1)
            x3.append(x2)
            X_.append(x3)
    # X_test = pickle.load('../data/test_matrix.out')
    Y_train = [1,0,0]*3
    # Y_test = [1,0,0]*3
    # print len(X_train) - len(Y_train)
    # print len(X_test) - len(Y_test)
    model = Sequential()
    model = get_nn_model()
    model.compile(loss='binary_crossentropy',
                optimizer='adam',
                metrics=['accuracy'])
    # model.fit(X_train, Y_train,
    #       batch_size=batch_size,
    #       nb_epoch=nb_epoch,
    #       validation_data=(X_test, Y_test))
#2
    model.fit(X_, Y_train,
          batch_size=batch_size,
          nb_epoch=nb_epoch,
          validation_split = 0.2)
    print 'ok'

    def create(self):
        language_model = Sequential()
        self.textual_embedding(language_model, mask_zero=True)
        self.language_model = language_model

        visual_model_factory = \
                select_sequential_visual_model[self._config.trainable_perception_name](
                    self._config.visual_dim)
        visual_model = visual_model_factory.create()
        visual_dimensionality = visual_model_factory.get_dimensionality()
        self.visual_embedding(visual_model, visual_dimensionality)
        #visual_model = Sequential()
        #self.visual_embedding(visual_model)
        # the below should contain all zeros
        zero_model = Sequential()
        zero_model.add(RepeatVector(self._config.max_input_time_steps)-1)
        visual_model.add(Merge[visual_model, zero_model], mode='concat')
        self.visual_model = visual_model

        if self._config.multimodal_merge_mode == 'dot':
            self.add(Merge([language_model, visual_model], mode='dot', dot_axes=[(1,),(1,)]))
        else:
            self.add(Merge([language_model, visual_model], mode=self._config.multimodal_merge_mode))

        self.add(self._config.recurrent_encoder(
            self._config.hidden_state_dim, 
            return_sequences=False,
            go_backwards=self._config.go_backwards))
        self.deep_mlp()
        self.add(Dense(self._config.output_dim))
        self.add(Activation('softmax'))

    def __init__(self, restore=None, session=None, Dropout=Dropout, num_labels=10):
        self.num_channels = 1
        self.image_size = 28
        self.num_labels = num_labels

        model = Sequential()

        nb_filters = 64
        layers = [Conv2D(nb_filters, (5, 5), strides=(2, 2), padding="same",
                         input_shape=(28, 28, 1)),
                  Activation('relu'),
                  Conv2D(nb_filters, (3, 3), strides=(2, 2), padding="valid"),
                  Activation('relu'),
                  Conv2D(nb_filters, (3, 3), strides=(1, 1), padding="valid"),
                  Activation('relu'),
                  Flatten(),
                  Dense(32),
                  Activation('relu'),
                  Dropout(.5),
                  Dense(num_labels)]

        for layer in layers:
            model.add(layer)

        if restore != None:
            model.load_weights(restore)
        
        self.model = model

class QLearn:
    def __init__(self, actions, epsilon, alpha, gamma):
        
        # instead of a dictionary, we'll be using
        #   a neural network
        # self.q = {}
        self.epsilon = epsilon  # exploration constant
        self.alpha = alpha      # discount constant
        self.gamma = gamma      # discount factor
        self.actions = actions
        
        # Build the neural network
        self.network = Sequential()
        self.network.add(Dense(50, init='lecun_uniform', input_shape=(4,)))
        # self.network.add(Activation('sigmoid'))
        #self.network.add(Dropout(0.2))

        self.network.add(Dense(20, init='lecun_uniform'))
        # self.network.add(Activation('sigmoid'))
        # #self.network.add(Dropout(0.2))

        self.network.add(Dense(2, init='lecun_uniform'))
        # self.network.add(Activation('linear')) #linear output so we can have range of real-valued outputs

        # rms = SGD(lr=0.0001, decay=1e-6, momentum=0.5) # explodes to non
        rms = RMSprop()
        # rms = Adagrad()
        # rms = Adam()
        self.network.compile(loss='mse', optimizer=rms)
        # Get a summary of the network
        self.network.summary()

def test_dropout(layer_class):
    for unroll in [True, False]:
        layer_test(layer_class,
                   kwargs={'units': units,
                           'dropout': 0.1,
                           'recurrent_dropout': 0.1,
                           'unroll': unroll},
                   input_shape=(num_samples, timesteps, embedding_dim))

        # Test that dropout is applied during training
        x = K.ones((num_samples, timesteps, embedding_dim))
        layer = layer_class(units, dropout=0.5, recurrent_dropout=0.5,
                            input_shape=(timesteps, embedding_dim))
        y = layer(x)
        assert y._uses_learning_phase

        y = layer(x, training=True)
        assert not getattr(y, '_uses_learning_phase')

        # Test that dropout is not applied during testing
        x = np.random.random((num_samples, timesteps, embedding_dim))
        layer = layer_class(units, dropout=0.5, recurrent_dropout=0.5,
                            unroll=unroll,
                            input_shape=(timesteps, embedding_dim))
        model = Sequential([layer])
        assert model.uses_learning_phase
        y1 = model.predict(x)
        y2 = model.predict(x)
        assert_allclose(y1, y2)

def Simple(layers, func, ipt):
    model = Sequential()
    #model.add(BatchNormalization(input_shape = [ipt]))
    model.add(Dense(layers[0], input_dim = ipt, activation = func[0]))
    for i in range(1, len(layers)):
        model.add(Dense(layers[i], activation = func[i]))
    return model

 def encoders_m(self, inputs):
     input_encoder_m = Sequential()
     input_encoder_m.add(Embedding(input_dim=self.vocab_size,
                                   output_dim=64))
     input_encoder_m.add(Dropout(0.3))
     encode_m = input_encoder_m(inputs)
     return encode_m

 def encoders_c(self, inputs):
     input_encoder_c = Sequential()
     input_encoder_c.add(Embedding(input_dim=self.vocab_size,
                                   output_dim=self.query_maxlen))
     input_encoder_c.add(Dropout(0.3))
     encoder_c = input_encoder_c(inputs)
     return encoder_c

def build_part1_RNN(window_size):

    model = Sequential()
    model.add(LSTM(5, input_shape=(window_size,1) ))
    #model.add(Dropout(0.5))
    model.add(Dense(1))
    return model

def build_part2_RNN(window_size, num_chars):

    model = Sequential()
    model.add(LSTM(200, input_shape=(window_size, num_chars)))
    #model.add(Dropout(0.5))
    model.add(Dense(num_chars, activation='softmax'))
    return model

def get_item_subgraph(input_shape, latent_dim):
    # Could take item metadata here, do convolutional layers etc.

    model = Sequential()
    model.add(Dense(latent_dim, input_shape=input_shape))

    return model

def create_model(kernel_regularizer=None, activity_regularizer=None):
    model = Sequential()
    model.add(Dense(num_classes,
                    kernel_regularizer=kernel_regularizer,
                    activity_regularizer=activity_regularizer,
                    input_shape=(data_dim,)))
    return model

def fork (model, n=2):
    forks = []
    for i in range(n):
        f = Sequential()
        f.add (model)
        forks.append(f)
    return forks

def build_partial_cnn1(img_rows, img_cols):
    model = Sequential()
    #model.add(Convolution2D(nb_filter=100, nb_row=5, nb_col=5,
    model.add(Convolution2D(nb_filter=10, nb_row=2, nb_col=2,
                            init='glorot_uniform', activation='linear',
                            border_mode='valid',
                            input_shape=(1, img_rows, img_cols)))
    model.add(Activation('relu'))

    #model.add(MaxPooling2D(pool_size=(2, 2)))

    #model.add(Convolution2D(nb_filter=100, nb_row=5, nb_col=5,
    '''model.add(Convolution2D(nb_filter=512, nb_row=5, nb_col=5,
                            init='glorot_uniform', activation='linear',
                            border_mode='valid'))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    #model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(256))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))'''

    return model