python类Flatten()的实例源码

def tsinalis(input_shape, n_classes):
    """
    Input size should be [batch, 1d, 2d, ch] = (None, 1, 15000, 1)
    """
    model = Sequential(name='Tsinalis')
    model.add(Conv1D (kernel_size = (200), filters = 20, input_shape=input_shape, activation='relu'))
    print(model.input_shape)
    print(model.output_shape)
    model.add(MaxPooling1D(pool_size = (20), strides=(10)))
    print(model.output_shape)
    model.add(keras.layers.core.Reshape([20,-1,1]))
    print(model.output_shape)    
    model.add(Conv2D (kernel_size = (20,30), filters = 400, activation='relu'))
    print(model.output_shape)
    model.add(MaxPooling2D(pool_size = (1,10), strides=(1,2)))
    print(model.output_shape)
    model.add(Flatten())
    print(model.output_shape)
    model.add(Dense (500, activation='relu'))
    model.add(Dense (500, activation='relu'))
    model.add(Dense(n_classes, activation = 'softmax',activity_regularizer=keras.regularizers.l2()  ))
    model.compile( loss='categorical_crossentropy', optimizer=keras.optimizers.SGD(), metrics=[keras.metrics.categorical_accuracy])
    return model

def create_Kao_Onet( weight_path = 'model48.h5'):
    input = Input(shape = [48,48,3])
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1,2],name='prelu1')(x)
    x = MaxPool2D(pool_size=3, strides=2, padding='same')(x)
    x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1,2],name='prelu2')(x)
    x = MaxPool2D(pool_size=3, strides=2)(x)
    x = Conv2D(64, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1,2],name='prelu3')(x)
    x = MaxPool2D(pool_size=2)(x)
    x = Conv2D(128, (2, 2), strides=1, padding='valid', name='conv4')(x)
    x = PReLU(shared_axes=[1,2],name='prelu4')(x)
    x = Permute((3,2,1))(x)
    x = Flatten()(x)
    x = Dense(256, name='conv5') (x)
    x = PReLU(name='prelu5')(x)

    classifier = Dense(2, activation='softmax',name='conv6-1')(x)
    bbox_regress = Dense(4,name='conv6-2')(x)
    landmark_regress = Dense(10,name='conv6-3')(x)
    model = Model([input], [classifier, bbox_regress, landmark_regress])
    model.load_weights(weight_path, by_name=True)

    return model

def make_teacher_model(train_data, validation_data, nb_epoch=3):
    '''Train a simple CNN as teacher model.
    '''
    model = Sequential()
    model.add(Conv2D(64, 3, 3, input_shape=input_shape,
                     border_mode='same', name='conv1'))
    model.add(MaxPooling2D(name='pool1'))
    model.add(Conv2D(64, 3, 3, border_mode='same', name='conv2'))
    model.add(MaxPooling2D(name='pool2'))
    model.add(Flatten(name='flatten'))
    model.add(Dense(64, activation='relu', name='fc1'))
    model.add(Dense(nb_class, activation='softmax', name='fc2'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=SGD(lr=0.01, momentum=0.9),
                  metrics=['accuracy'])

    train_x, train_y = train_data
    history = model.fit(train_x, train_y, nb_epoch=nb_epoch,
                        validation_data=validation_data)
    return model, history

def build_network(num_actions, agent_history_length, resized_width, resized_height):
    state = tf.placeholder("float", [None, agent_history_length, resized_width, resized_height])

    inputs_v = Input(shape=(agent_history_length, resized_width, resized_height,))
    #model_v  = Permute((2, 3, 1))(inputs_v)

    model_v = Convolution2D(nb_filter=16, nb_row=8, nb_col=8, subsample=(4,4), activation='relu', border_mode='same')(inputs_v)
    model_v = Convolution2D(nb_filter=32, nb_row=4, nb_col=4, subsample=(2,2), activation='relu', border_mode='same')(model_v)
    model_v = Flatten()(model_v)
    model_v = Dense(output_dim=512)(model_v)
    model_v = PReLU()(model_v)


    action_probs = Dense(name="p", output_dim=num_actions, activation='softmax')(model_v)

    state_value = Dense(name="v", output_dim=1, activation='linear')(model_v)


    value_network = Model(input=inputs_v, output=[state_value, action_probs])


    return state, value_network

def build_simpleCNN(input_shape = (32, 32, 3), num_output = 10):

    h, w, nch = input_shape
    assert h == w, 'expect input shape (h, w, nch), h == w'

    images = Input(shape = (h, h, nch))
    x = Conv2D(64, (4, 4), strides = (1, 1),
               kernel_initializer = init, padding = 'same')(images)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size = (2, 2))(x)
    x = Conv2D(128, (4, 4), strides = (1, 1),
               kernel_initializer = init, padding = 'same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size = (2, 2))(x)
    x = Flatten()(x)
    outputs = Dense(num_output, kernel_initializer = init,
                    activation = 'softmax')(x)

    model = Model(inputs = images, outputs = outputs)
    return model

def model_discriminator(input_shape=(1, 28, 28), dropout_rate=0.5):
    d_input = dim_ordering_input(input_shape, name="input_x")
    nch = 512
    # nch = 128
    H = Convolution2D(int(nch / 2), 5, 5, subsample=(2, 2), border_mode='same', activation='relu')(d_input)
    H = LeakyReLU(0.2)(H)
    H = Dropout(dropout_rate)(H)
    H = Convolution2D(nch, 5, 5, subsample=(2, 2), border_mode='same', activation='relu')(H)
    H = LeakyReLU(0.2)(H)
    H = Dropout(dropout_rate)(H)
    H = Flatten()(H)
    H = Dense(int(nch / 2))(H)
    H = LeakyReLU(0.2)(H)
    H = Dropout(dropout_rate)(H)
    d_V = Dense(1, activation='sigmoid')(H)
    return Model(d_input, d_V)

def create_Kao_Rnet (weight_path = 'model24.h5'):
    input = Input(shape=[24, 24, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    x = Conv2D(28, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = PReLU(shared_axes=[1, 2], name='prelu1')(x)
    x = MaxPool2D(pool_size=3,strides=2, padding='same')(x)

    x = Conv2D(48, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = PReLU(shared_axes=[1, 2], name='prelu2')(x)
    x = MaxPool2D(pool_size=3, strides=2)(x)

    x = Conv2D(64, (2, 2), strides=1, padding='valid', name='conv3')(x)
    x = PReLU(shared_axes=[1, 2], name='prelu3')(x)
    x = Permute((3, 2, 1))(x)
    x = Flatten()(x)
    x = Dense(128, name='conv4')(x)
    x = PReLU( name='prelu4')(x)
    classifier = Dense(2, activation='softmax', name='conv5-1')(x)
    bbox_regress = Dense(4, name='conv5-2')(x)
    model = Model([input], [classifier, bbox_regress])
    model.load_weights(weight_path, by_name=True)
    return model

def fGRU_avg(MAX_NB_WORDS, MAX_WORDS, MAX_SENTS, EMBEDDING_DIM, WORDGRU, embedding_matrix, DROPOUTPER):
    wordInputs = Input(shape=(MAX_SENTS+1, MAX_WORDS), name="wordInputs", dtype='float32')

    wordInp = Flatten()(wordInputs)

    wordEmbedding = Embedding(MAX_NB_WORDS, EMBEDDING_DIM, weights=[embedding_matrix], mask_zero=False, trainable=True, name='wordEmbedding')(wordInp) 

    hij = Bidirectional(GRU(WORDGRU, return_sequences=True), name='gru1')(wordEmbedding)

    head = GlobalAveragePooling1D()(hij) 

    v6 = Dense(1, activation="sigmoid", name="dense")(head)

    model = Model(inputs=[wordInputs] , outputs=[v6])
    model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

    return model

def fGlove_avg(MAX_NB_WORDS, MAX_WORDS, MAX_SENTS, EMBEDDING_DIM, WORDGRU, embedding_matrix, DROPOUTPER):
    wordInputs = Input(shape=(MAX_SENTS+1, MAX_WORDS), name="wordInputs", dtype='float32')

    wordInp = Flatten()(wordInputs)

    wordEmbedding = Embedding(MAX_NB_WORDS, EMBEDDING_DIM, weights=[embedding_matrix], mask_zero=False, trainable=True, name='wordEmbedding')(wordInp) 

    head = GlobalAveragePooling1D()(wordEmbedding) 


    v6 = Dense(1, activation="sigmoid", name="dense")(head)

    model = Model(inputs=[wordInputs] , outputs=[v6])
    model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

    return model

def classifier(base_layers, input_rois, batch_size, nb_classes = 3, trainable=False):

    # compile times tend to be very high, so we use smaller ROI pooling regions to workaround

    if K.backend() == 'tensorflow':
        pooling_regions = 14
        input_shape = (batch_size,14,14,2048)
    elif K.backend() == 'theano':
        pooling_regions = 7
        input_shape = (batch_size,2048,7,7)

    out_roi_pool = RoiPoolingConv(pooling_regions, batch_size)([base_layers, input_rois])
    out = TimeDistributed(Flatten())(out_roi_pool)
#    out = TimeDistributed(Dropout(0.4))(out)
#    out = TimeDistributed(Dense(2048,activation='relu'))(out)

    out_class = TimeDistributed(Dense(nb_classes, activation='softmax', kernel_initializer='zero'), name='dense_class_{}'.format(nb_classes))(out)
    # note: no regression target for bg class
    out_regr = TimeDistributed(Dense(4 * nb_classes, activation='linear', kernel_initializer='zero'), name='dense_regress_{}'.format(nb_classes))(out)
    return [out_class, out_regr]

def classifier(base_layers, input_rois, batch_size, nb_classes = 3, trainable=False):

    # compile times tend to be very high, so we use smaller ROI pooling regions to workaround

    if K.backend() == 'tensorflow':
        pooling_regions = 14
        input_shape = (batch_size,14,14,512)
    elif K.backend() == 'theano':
        pooling_regions = 7
        input_shape = (batch_size,512,7,7)

    out_roi_pool = RoiPoolingConv(pooling_regions, batch_size)([base_layers, input_rois])
    out = TimeDistributed(Flatten())(out_roi_pool)
    out = TimeDistributed(Dense(4096,activation='relu'))(out)
    out = TimeDistributed(Dropout(0.5))(out)
    out = TimeDistributed(Dense(4096,activation='relu'))(out)
    out = TimeDistributed(Dropout(0.5))(out)

    out_class = TimeDistributed(Dense(nb_classes, activation='softmax', kernel_initializer='zero'), name='dense_class_{}'.format(nb_classes))(out)
    # note: no regression target for bg class
    out_regr = TimeDistributed(Dense(4 * nb_classes, activation='linear', kernel_initializer='zero'), name='dense_regress_{}'.format(nb_classes))(out)
    return [out_class, out_regr]

def classifier(base_layers, input_rois, batch_size, nb_classes = 3, trainable=False):

    # compile times tend to be very high, so we use smaller ROI pooling regions to workaround
    if K.backend() == 'tensorflow':
        pooling_regions = 14
        input_shape = (batch_size,14,14,1024)
    elif K.backend() == 'theano':
        pooling_regions = 7
        input_shape = (batch_size,1024,7,7)

    out_roi_pool = RoiPoolingConv(pooling_regions, batch_size)([base_layers, input_rois])
    out = TimeDistributed(Flatten())(out_roi_pool)
    out = TimeDistributed(Dense(4096,activation='relu'))(out)
    out = TimeDistributed(Dropout(0.5))(out)
    out = TimeDistributed(Dense(4096,activation='relu'))(out)
    out = TimeDistributed(Dropout(0.5))(out)

    out_class = TimeDistributed(Dense(nb_classes, activation='softmax', kernel_initializer='zero'), name='dense_class_{}'.format(nb_classes))(out)
    # note: no regression target for bg class
    out_regr = TimeDistributed(Dense(4 * nb_classes, activation='linear', kernel_initializer='zero'), name='dense_regress_{}'.format(nb_classes))(out)
    return [out_class, out_regr]

def classifier(base_layers, input_rois, num_rois, nb_classes = 21, trainable=False):

    # compile times on theano tend to be very high, so we use smaller ROI pooling regions to workaround

    if K.backend() == 'tensorflow':
        pooling_regions = 7
        input_shape = (num_rois,7,7,512)
    elif K.backend() == 'theano':
        pooling_regions = 7
        input_shape = (num_rois,512,7,7)

    out_roi_pool = RoiPoolingConv(pooling_regions, num_rois)([base_layers, input_rois])

    out = TimeDistributed(Flatten(name='flatten'))(out_roi_pool)
    out = TimeDistributed(Dense(4096, activation='relu', name='fc1'))(out)
    out = TimeDistributed(Dropout(0.5))(out)
    out = TimeDistributed(Dense(4096, activation='relu', name='fc2'))(out)
    out = TimeDistributed(Dropout(0.5))(out)

    out_class = TimeDistributed(Dense(nb_classes, activation='softmax', kernel_initializer='zero'), name='dense_class_{}'.format(nb_classes))(out)
    # note: no regression target for bg class
    out_regr = TimeDistributed(Dense(4 * (nb_classes-1), activation='linear', kernel_initializer='zero'), name='dense_regress_{}'.format(nb_classes))(out)

    return [out_class, out_regr]

def create_actor_network(self, state_size, action_dim):
        """Create actor network."""
        print ("[MESSAGE] Build actor network.""")
        S = Input(shape=state_size)
        h_0 = Conv2D(32, (3, 3), padding="same",
                     kernel_regularizer=l2(0.0001),
                     activation="relu")(S)
        h_1 = Conv2D(32, (3, 3), padding="same",
                     kernel_regularizer=l2(0.0001),
                     activation="relu")(h_0)
        h_1 = AveragePooling2D(2, 2)(h_1)
        h_1 = Flatten()(h_1)
        h_1 = Dense(600, activation="relu")(h_1)
        A = Dense(action_dim, activation="softmax")(h_1)

        model = Model(inputs=S, outputs=A)

        return model, model.trainable_weights, S

def make_discriminator():
    """Creates a discriminator model that takes an image as input and outputs a single value, representing whether
    the input is real or generated. Unlike normal GANs, the output is not sigmoid and does not represent a probability!
    Instead, the output should be as large and negative as possible for generated inputs and as large and positive
    as possible for real inputs.

    Note that the improved WGAN paper suggests that BatchNormalization should not be used in the discriminator."""
    model = Sequential()
    if K.image_data_format() == 'channels_first':
        model.add(Convolution2D(64, (5, 5), padding='same', input_shape=(1, 28, 28)))
    else:
        model.add(Convolution2D(64, (5, 5), padding='same', input_shape=(28, 28, 1)))
    model.add(LeakyReLU())
    model.add(Convolution2D(128, (5, 5), kernel_initializer='he_normal', strides=[2, 2]))
    model.add(LeakyReLU())
    model.add(Convolution2D(128, (5, 5), kernel_initializer='he_normal', padding='same', strides=[2, 2]))
    model.add(LeakyReLU())
    model.add(Flatten())
    model.add(Dense(1024, kernel_initializer='he_normal'))
    model.add(LeakyReLU())
    model.add(Dense(1, kernel_initializer='he_normal'))
    return model

def make_model(dense_layer_sizes, filters, kernel_size, pool_size):
    '''Creates model comprised of 2 convolutional layers followed by dense layers

    dense_layer_sizes: List of layer sizes.
        This list has one number for each layer
    filters: Number of convolutional filters in each convolutional layer
    kernel_size: Convolutional kernel size
    pool_size: Size of pooling area for max pooling
    '''

    model = Sequential()
    model.add(Conv2D(filters, kernel_size,
                     padding='valid',
                     input_shape=input_shape))
    model.add(Activation('relu'))
    model.add(Conv2D(filters, kernel_size))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=pool_size))
    model.add(Dropout(0.25))

    model.add(Flatten())
    for layer_size in dense_layer_sizes:
        model.add(Dense(layer_size))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy',
                  optimizer='adadelta',
                  metrics=['accuracy'])

    return model

def make_teacher_model(train_data, validation_data, epochs=3):
    '''Train a simple CNN as teacher model.
    '''
    model = Sequential()
    model.add(Conv2D(64, 3, input_shape=input_shape,
                     padding='same', name='conv1'))
    model.add(MaxPooling2D(2, name='pool1'))
    model.add(Conv2D(64, 3, padding='same', name='conv2'))
    model.add(MaxPooling2D(2, name='pool2'))
    model.add(Flatten(name='flatten'))
    model.add(Dense(64, activation='relu', name='fc1'))
    model.add(Dense(num_class, activation='softmax', name='fc2'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=SGD(lr=0.01, momentum=0.9),
                  metrics=['accuracy'])

    train_x, train_y = train_data
    history = model.fit(train_x, train_y,
                        epochs=epochs,
                        validation_data=validation_data)
    return model, history

def load_model(input_shape, num_classes):
    model = Sequential()

    model.add(Convolution2D(6, kernel_size=(3, 3), activation='relu', input_shape=input_shape, padding="same"))
    model.add(Convolution2D(32, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Convolution2D(64, kernel_size=(3, 3), border_mode='same', activation='relu'))
    model.add(Convolution2D(64, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))

    return model

def predict(model, img):

    #Flatten it
    image = np.array(img).flatten()


    # float32
    image = image.astype('float32') 

    # normalize it
    image = image / 255

    # reshape for NN
    rimage = image.reshape(1, img_rows, img_colms,img_channels)

    # Now feed it to the NN, to fetch the predictions
    clas = model.predict(rimage)
    #prob_array = model.predict_proba(rimage)

    return  clas

def test_valid_workflow(self):
        # Create image URI dataframe
        label_cardinality = 10
        image_uri_df = self._create_train_image_uris_and_labels(
            repeat_factor=3, cardinality=label_cardinality)

        # We need a small model so that machines with limited resources can run it
        model = Sequential()
        model.add(Flatten(input_shape=(299, 299, 3)))
        model.add(Dense(label_cardinality))
        model.add(Activation("softmax"))

        estimator = self._get_estimator(model)
        self.assertTrue(estimator._validateParams())
        transformers = estimator.fit(image_uri_df)
        self.assertEqual(1, len(transformers))
        self.assertIsInstance(transformers[0]['transformer'], KerasImageFileTransformer)

def train(img_shape):
    classes = ['ALB', 'BET', 'DOL', 'LAG', 'NoF', 'OTHER', 'SHARK', 'YFT']

    # Model
    model = Sequential()
    model.add(Convolution2D(
        32, 3, 3, input_shape=img_shape, activation='relu', W_constraint=maxnorm(3)))
    model.add(Dropout(0.2))
    model.add(Convolution2D(32, 3, 3, activation='relu', W_constraint=maxnorm(3)))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(512, activation='relu', W_constraint=maxnorm(3)))
    model.add(Dropout(0.5))
    model.add(Dense(len(classes), activation='softmax'))

    features, labels = get_featurs_labels(img_shape)

    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    model.fit(features, labels, nb_epoch=10, batch_size=32, validation_split=0.2, verbose=1)
    return model

def test_highway_layers():
    n_highway_layers = 5
    x = Input(shape=(8,), dtype="int32")
    v = Embedding(input_dim=2, output_dim=10)(x)
    v = Flatten()(v)
    assert hasattr(v, "_keras_shape")
    v = highway_layers(v, n_layers=n_highway_layers)
    output = Dense(1)(v)
    model = Model(inputs=[x], outputs=[output])
    assert len(model.layers) > n_highway_layers * 3
    x = np.array([
        [1] + [0] * 7,
        [0] * 8,
        [0] * 7 + [1]])
    y = np.array([0, 1, 0])
    model.compile("rmsprop", "mse")
    model.fit(x, y, epochs=10)
    pred = model.predict(x)
    mean_diff = np.abs(pred - y).mean()
    assert mean_diff < 0.5, pred

def create_network():
    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Flatten
    from keras.layers import Conv2D, MaxPooling2D

    model = Sequential()
    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=INPUT_SHAPE))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(NUM_CLASSES, activation='softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return KerasNetwork(model, 'cnn_weights.hd5')

def create_conv_model(self):
        # This is the place where neural network model initialized
        init = 'glorot_uniform'
        self.state_in = Input(self.state_dim)
        self.l1 = Convolution2D(32, 8, 8, activation='elu', init=init, subsample=(4, 4), border_mode='same')(
            self.state_in)
        self.l2 = Convolution2D(64, 4, 4, activation='elu', init=init, subsample=(2, 2), border_mode='same')(
            self.l1)
        # self.l3 = Convolution2D(64, 3, 3, activation='relu', init=init, subsample=(1, 1), border_mode='same')(
        #     self.l2)
        self.l3 = self.l2
        self.h = Flatten()(self.l3)
        self.hidden = Dense(256, init=init, activation='elu')(self.h)
        self.value = Dense(1, init=init)(self.hidden)
        self.policy = Dense(self.action_dim, init=init, activation='softmax')(self.hidden)
        self.q_values = self.entropy_coef * (Theano.log(self.policy + 1e-18) -
                                             Theano.tile(Theano.sum(Theano.log(self.policy + 1e-18) * self.policy,
                                                                    axis=[1], keepdims=True), (1, self.action_dim)))
        self.q_values = self.q_values + Theano.tile(self.value, (1, self.action_dim))
        self.model = Model(self.state_in, output=[self.policy, self.value])

def make_model(state_shape, n_actions):
    in_t = Input(shape=(HISTORY_STEPS,) + state_shape, name='input')
    action_t = Input(shape=(1,), dtype='int32', name='action')
    advantage_t = Input(shape=(1,), name='advantage')

    fl_t = Flatten(name='flat')(in_t)
    l1_t = Dense(SIMPLE_L1_SIZE, activation='relu', name='l1')(fl_t)
    l2_t = Dense(SIMPLE_L2_SIZE, activation='relu', name='l2')(l1_t)
    policy_t = Dense(n_actions, name='policy', activation='softmax')(l2_t)

    def loss_func(args):
        p_t, act_t, adv_t = args
        oh_t = K.one_hot(act_t, n_actions)
        oh_t = K.squeeze(oh_t, 1)
        p_oh_t = K.log(1e-6 + K.sum(oh_t * p_t, axis=-1, keepdims=True))
        res_t = adv_t * p_oh_t
        return -res_t

    loss_t = Lambda(loss_func, output_shape=(1,), name='loss')([policy_t, action_t, advantage_t])

    return Model(input=[in_t, action_t, advantage_t], output=[policy_t, loss_t])

def net_input(env):
    """
    Create input part of the network with optional prescaling.
    :return: input_tensor, output_tensor
    """
    in_t = Input(shape=env.observation_space.shape, name='input')
    out_t = Conv2D(32, 5, 5, activation='relu', border_mode='same')(in_t)
    out_t = MaxPooling2D((2, 2))(out_t)
    out_t = Conv2D(32, 5, 5, activation='relu', border_mode='same')(out_t)
    out_t = MaxPooling2D((2, 2))(out_t)
    out_t = Conv2D(64, 4, 4, activation='relu', border_mode='same')(out_t)
    out_t = MaxPooling2D((2, 2))(out_t)
    out_t = Conv2D(64, 3, 3, activation='relu', border_mode='same')(out_t)
    out_t = Flatten(name='flat')(out_t)
    out_t = Dense(512, name='l1', activation='relu')(out_t)

    return in_t, out_t

def make_teacher_model(train_data, validation_data, epochs=3):
    '''Train a simple CNN as teacher model.
    '''
    model = Sequential()
    model.add(Conv2D(64, 3, input_shape=input_shape,
                     padding='same', name='conv1'))
    model.add(MaxPooling2D(2, name='pool1'))
    model.add(Conv2D(64, 3, padding='same', name='conv2'))
    model.add(MaxPooling2D(2, name='pool2'))
    model.add(Flatten(name='flatten'))
    model.add(Dense(64, activation='relu', name='fc1'))
    model.add(Dense(num_class, activation='softmax', name='fc2'))
    model.compile(loss='categorical_crossentropy',
                  optimizer=SGD(lr=0.01, momentum=0.9),
                  metrics=['accuracy'])

    train_x, train_y = train_data
    history = model.fit(train_x, train_y,
                        epochs=epochs,
                        validation_data=validation_data)
    return model, history

def build_model(self):
        initializer = initializers.random_normal(stddev=0.02)
        model = Sequential()
        if self.padding:
            model.add(ZeroPadding2D(padding=(1, 0), data_format="channels_first", input_shape=(self.layers, self.rows, self.columns)))
        model.add(Conv2D(32, (8, 8), activation="relu", data_format="channels_first",
                         strides=(4, 4), kernel_initializer=initializer, padding='same',
                         input_shape=(self.layers, self.rows, self.columns)))
        model.add(Conv2D(64, (4, 4), activation="relu", data_format="channels_first", strides=(2, 2),
                         kernel_initializer=initializer, padding='same'))
        model.add(Conv2D(64, (3, 3), activation="relu", data_format="channels_first", strides=(1, 1),
                         kernel_initializer=initializer, padding='same'))
        model.add(Flatten())
        model.add(Dense(512, activation="relu", kernel_initializer=initializer))
        model.add(Dense(self.actions_num, kernel_initializer=initializer))

        adam = Adam(lr=1e-6)
        model.compile(loss='mse', optimizer=adam)
        return model

def test_model_pipe_keras(self):
        model = Sequential()
        model.add(Flatten(input_shape=(1, 28, 28)))
        model.add(Dense(128, activation='relu'))
        model.add(Dense(10, activation='softmax'))

        p = model_util.ModelPipe()
        input_data = [np.random.random((1, 1, 28, 28)) for _ in range(2)]

        p.add(model.predict, batch_size=64, batcher=np.vstack)

        expected_output = [
            model.predict(
                x.reshape(
                    (1, 1, 28, 28))) for x in input_data]
        output = p.apply_ordered(input_data)

        self.assertTrue(np.isclose(np.array(output).flatten(),
                                   np.array(expected_output).flatten()).all())

def build_discriminator(self):

        model = Sequential()

        model.add(Conv2D(64, kernel_size=3, strides=2, input_shape=self.missing_shape, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Conv2D(256, kernel_size=3, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Flatten())
        model.add(Dense(1, activation='sigmoid'))
        model.summary()

        img = Input(shape=self.missing_shape)
        validity = model(img)

        return Model(img, validity)