Question

基于this的帖子，我试图创建另一个模型，在该模型中添加分类变量和连续变量。请在下面找到代码：

from __future__ import print_function
import pandas as pd; 
import tensorflow as tf
import numpy as np
from sklearn.preprocessing import LabelEncoder

if __name__  == '__main__':

    # 1 categorical input feature and a binary output
    df = pd.DataFrame({'cat2': np.array(['o', 'm', 'm', 'c', 'c', 'c', 'o', 'm', 'm', 'm']),
                       'num1': np.random.rand(10),
                       'label': np.array([0, 0, 1, 1, 0, 0, 1, 0, 1, 1])})

    encoder = LabelEncoder()
    encoder.fit(df.cat2.values)

    X1 = encoder.transform(df.cat2.values).reshape(-1,1)
    X2 = np.array(df.num1.values).reshape(-1,1)
#     X = np.concatenate((X1,X2), axis=1)
    Y = np.zeros((len(df), 2))
    Y[np.arange(len(df)), df.label.values] = 1

    # Neural net parameters
    training_epochs = 5
    learning_rate = 1e-3
    cardinality = len(np.unique(X))
    embedding_size = 2
    input_X_size = 1
    n_labels = len(np.unique(Y))
    n_hidden = 10

    # Placeholders for input, output
    cat2 = tf.placeholder(tf.int32, [None], name='cat2')
    x = tf.placeholder(tf.float32, [None, 1], name="input_x")
    y = tf.placeholder(tf.float32, [None, 2], name="input_y")

    embed_matrix = tf.Variable(
                tf.random_uniform([cardinality, embedding_size], -1.0, 1.0),
                name="embed_matrix"
            )
    embed = tf.nn.embedding_lookup(embed_matrix, cat2)

    inputs_with_embed = tf.concat([x, embedding_aggregated], axis=2, name="inputs_with_embed")

    # Neural network weights

    h = tf.get_variable(name='h2', shape=[inputs_with_embed, n_hidden],
                        initializer=tf.contrib.layers.xavier_initializer())
    W_out = tf.get_variable(name='out_w', shape=[n_hidden, n_labels],
                            initializer=tf.contrib.layers.xavier_initializer())

    # Neural network operations
    #embedded_chars = tf.nn.embedding_lookup(embeddings, x)

    layer_1 = tf.matmul(inputs_with_embed,h)
    layer_1 = tf.nn.relu(layer_1)
    out_layer = tf.matmul(layer_1, W_out)

    # Define loss and optimizer
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out_layer, labels=y))
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

    # Initializing the variables
    init = tf.global_variables_initializer()

    # Launch the graph
    with tf.Session() as sess:
        sess.run(init)

        for epoch in range(training_epochs):
            avg_cost = 0.

            # Run optimization op (backprop) and cost op (to get loss value)
            _, c = sess.run([optimizer, cost],
                             feed_dict={x: X2,cat2:X1, y: Y})
    print("Optimization Finished!")

但是出现以下错误。看来我没有串联连续变量并正确嵌入。但是我不知道如何解决它。

请有人指导我。

ValueError: Shape must be at least rank 3 but is rank 2 for 'inputs_with_embed_2' (op: 'ConcatV2') with input shapes: [?,1], [?,2], [] and with computed input tensors: input[2] = <2>.

谢谢！

Answer 1

如果用embedding_agregated表示embed（可能是错字）

错误是您的情况下没有axis=2，应该是axis=1

inputs_with_embed = tf.concat([x, embed], axis=1, name="inputs_with_embed")

embed的形状为[None，embedding_dimension]，而x的形状为[None，1]

它们都是2D张量，因此您可以访问axis = 0或axis = 1（在0而不是1处索引），因此要使input_with_embed的形状为[None，embedding_dimension + 1]，您需要连上axis=1

使用连续和分类变量的Tensorflow嵌入

1 个答案: