def __init__(self, field_sizes=None, embed_size=10, filter_sizes=None, layer_acts=None, drop_out=None, init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None): Model.__init__(self) init_vars = [] num_inputs = len(field_sizes) for i in range(num_inputs): init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype)) init_vars.append(('f1', [embed_size, filter_sizes[0], 1, 2], 'xavier', dtype)) init_vars.append(('f2', [embed_size, filter_sizes[1], 2, 2], 'xavier', dtype)) init_vars.append(('w1', [2 * 3 * embed_size, 1], 'xavier', dtype)) init_vars.append(('b1', [1], 'zero', dtype)) self.graph = tf.Graph() with self.graph.as_default(): if random_seed is not None: tf.set_random_seed(random_seed) self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)] self.y = tf.placeholder(dtype) self.keep_prob_train = 1 - np.array(drop_out) self.keep_prob_test = np.ones_like(drop_out) self.layer_keeps = tf.placeholder(dtype) self.vars = init_var_map(init_vars, init_path) w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)] xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)], 1) l = xw l = tf.transpose(tf.reshape(l, [-1, num_inputs, embed_size, 1]), [0, 2, 1, 3]) f1 = self.vars['f1'] l = tf.nn.conv2d(l, f1, [1, 1, 1, 1], 'SAME') l = tf.transpose( max_pool_4d( tf.transpose(l, [0, 1, 3, 2]), int(num_inputs / 2)), [0, 1, 3, 2]) f2 = self.vars['f2'] l = tf.nn.conv2d(l, f2, [1, 1, 1, 1], 'SAME') l = tf.transpose( max_pool_4d( tf.transpose(l, [0, 1, 3, 2]), 3), [0, 1, 3, 2]) l = tf.nn.dropout( activate( tf.reshape(l, [-1, embed_size * 3 * 2]), layer_acts[0]), self.layer_keeps[0]) w1 = self.vars['w1'] b1 = self.vars['b1'] l = tf.matmul(l, w1) + b1 l = tf.squeeze(l) self.y_prob = tf.sigmoid(l) self.loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y)) self.optimizer = get_optimizer(opt_algo, learning_rate, self.loss) config = tf.ConfigProto() config.gpu_options.allow_growth = True self.sess = tf.Session(config=config) tf.global_variables_initializer().run(session=self.sess)
def __init__(self, field_sizes=None, embed_size=10, filter_sizes=None, layer_acts=None, drop_out=None, init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None): Model.__init__(self) init_vars = [] num_inputs = len(field_sizes) for i in range(num_inputs): init_vars.append(('embed_%d' % i, [field_sizes[i], embed_size], 'xavier', dtype)) init_vars.append(('f1', [embed_size, filter_sizes[0], 1, 2], 'xavier', dtype)) init_vars.append(('f2', [embed_size, filter_sizes[1], 2, 2], 'xavier', dtype)) init_vars.append(('w1', [2 * 3 * embed_size, 1], 'xavier', dtype)) init_vars.append(('b1', [1], 'zero', dtype)) print('init_vars: ', init_vars) self.graph = tf.Graph() with self.graph.as_default(): if random_seed is not None: tf.set_random_seed(random_seed) self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)] self.y = tf.placeholder(dtype) self.keep_prob_train = 1 - np.array(drop_out) self.keep_prob_test = np.ones_like(drop_out) self.layer_keeps = tf.placeholder(dtype) self.vars = utils.init_var_map(init_vars, init_path) w0 = [self.vars['embed_%d' % i] for i in range(num_inputs)] xw = tf.concat([tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) for i in range(num_inputs)], 1) l = xw l = tf.transpose(tf.reshape(l, [-1, num_inputs, embed_size, 1]), [0, 2, 1, 3]) # 变为 16 x 10 矩阵 f1 = self.vars['f1'] l = tf.nn.conv2d(l, f1, [1, 1, 1, 1], 'SAME') l = tf.transpose( utils.max_pool_4d( tf.transpose(l, [0, 1, 3, 2]), int(num_inputs / 2)), [0, 1, 3, 2]) f2 = self.vars['f2'] l = tf.nn.conv2d(l, f2, [1, 1, 1, 1], 'SAME') l = tf.transpose( utils.max_pool_4d( tf.transpose(l, [0, 1, 3, 2]), 3), [0, 1, 3, 2]) l = tf.nn.dropout( utils.activate( tf.reshape(l, [-1, embed_size * 3 * 2]), layer_acts[0]), self.layer_keeps[0]) w1 = self.vars['w1'] b1 = self.vars['b1'] l = tf.matmul(l, w1) + b1 l = tf.squeeze(l) self.y_prob = tf.sigmoid(l) self.loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=l, labels=self.y)) self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss) config = tf.ConfigProto() config.gpu_options.allow_growth = True self.sess = tf.Session(config=config) tf.global_variables_initializer().run(session=self.sess)
def __init__(self, layer_sizes=None, layer_acts=None, layer_keeps=None, init_path=None, opt_algo='gd', learning_rate=1e-2, random_seed=None): init_vars = [] num_inputs = len(layer_sizes[0]) embedding_order = layer_sizes[1] for i in range(num_inputs): layer_input = layer_sizes[0][i] layer_output = embedding_order init_vars.append(('w0_%d' % i, [layer_input, layer_output], 'tnormal', dtype)) init_vars.append(('b0_%d' % i, [layer_output], 'zero', dtype)) init_vars.append(('f1', [embedding_order, layer_sizes[2], 1, 2], 'tnormal', dtype)) init_vars.append(('f2', [embedding_order, layer_sizes[3], 2, 2], 'tnormal', dtype)) init_vars.append(('w1', [2 * 3 * embedding_order, 1], 'tnormal', dtype)) init_vars.append(('b1', [1], 'zero', dtype)) self.graph = tf.Graph() with self.graph.as_default(): if random_seed is not None: tf.set_random_seed(random_seed) self.X = [tf.sparse_placeholder(dtype) for i in range(num_inputs)] self.y = tf.placeholder(dtype) self.vars = utils.init_var_map(init_vars, init_path) w0 = [self.vars['w0_%d' % i] for i in range(num_inputs)] b0 = [self.vars['b0_%d' % i] for i in range(num_inputs)] l = tf.nn.dropout( utils.activate( tf.concat(1, [ tf.sparse_tensor_dense_matmul(self.X[i], w0[i]) + b0[i] for i in range(num_inputs) ]), layer_acts[0]), layer_keeps[0]) l = tf.transpose( tf.reshape(l, [-1, num_inputs, embedding_order, 1]), [0, 2, 1, 3]) f1 = self.vars['f1'] l = tf.nn.conv2d(l, f1, [1, 1, 1, 1], 'SAME') l = tf.transpose( utils.max_pool_4d(tf.transpose(l, [0, 1, 3, 2]), num_inputs / 2), [0, 1, 3, 2]) f2 = self.vars['f2'] l = tf.nn.conv2d(l, f2, [1, 1, 1, 1], 'SAME') l = tf.transpose( utils.max_pool_4d(tf.transpose(l, [0, 1, 3, 2]), 3), [0, 1, 3, 2]) l = tf.nn.dropout( utils.activate(tf.reshape(l, [-1, embedding_order * 3 * 2]), layer_acts[1]), layer_keeps[1]) w1 = self.vars['w1'] b1 = self.vars['b1'] l = tf.nn.dropout( utils.activate(tf.matmul(l, w1) + b1, layer_acts[2]), layer_keeps[2]) self.y_prob = tf.sigmoid(l) self.loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(l, self.y)) self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss) config = tf.ConfigProto() config.gpu_options.allow_growth = True self.sess = tf.Session(config=config) tf.initialize_all_variables().run(session=self.sess)