【发布时间】：2020-10-14 07:15:59
【问题描述】：
这来自 Keras 文档示例：训练模型以计算 priority_score 以及要转发电子邮件的部门。
我以另一种方式实现模型，我可以编译它，但我无法训练模型。我猜这是模型 I/O 问题，即我需要提供正确格式的 I/O 数据。
ValueError: Failed to find data adapter that can handle input: (<class 'dict'> containing {"<class 'str'>"} keys and {"<class 'numpy.ndarray'>", '(<class \'list\'> containing values of types {"<class \'str\'>"})'} values), (<class 'dict'> containing {"<class 'str'>"} keys and {"<class 'numpy.ndarray'>"} values)
因为太长了，所以没有放在这个帖子的标题里。
我的模型有 3 个输入：
- title_input：应该是单个字符串
- body_input：应该是单个字符串
- tags_input：12 个 0 或 1 的数组。例如，[0,1,0,1,0,0,0,0,0,1]
输出是：
- 优先级：浮动
- 部门：由 4 个 0,1 组成的数组。
问题
谁能告诉我我的代码有什么问题？
一般来说，我应该如何考虑模型的 I/O？比如这个案例。我以为准备N个字符串，比如800个字符串，800个标签就OK了。但我不断收到错误。好吧，我解决了大多数问题，但无法克服这一问题。请分享您的经验。谢谢！
附录
模型总结
Model: "functional_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
tags_input (InputLayer) [(None, 12)] 0
__________________________________________________________________________________________________
flatten (Flatten) (None, 12) 0 tags_input[0][0]
__________________________________________________________________________________________________
title_input (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
body_input (InputLayer) [(None, 1)] 0
__________________________________________________________________________________________________
dense (Dense) (None, 500) 6500 flatten[0][0]
__________________________________________________________________________________________________
text_vectorization (TextVectori (None, 500) 0 title_input[0][0]
__________________________________________________________________________________________________
text_vectorization_1 (TextVecto (None, 500) 0 body_input[0][0]
__________________________________________________________________________________________________
tf_op_layer_ExpandDims (TensorF [(None, 500, 1)] 0 dense[0][0]
__________________________________________________________________________________________________
embedding (Embedding) (None, 500, 100) 1000100 text_vectorization[0][0]
__________________________________________________________________________________________________
embedding_1 (Embedding) (None, 500, 100) 1000100 text_vectorization_1[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 500, 100) 200 tf_op_layer_ExpandDims[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 500, 300) 0 embedding[0][0]
embedding_1[0][0]
dense_1[0][0]
__________________________________________________________________________________________________
priority (Dense) (None, 500, 1) 301 concatenate[0][0]
__________________________________________________________________________________________________
departments (Dense) (None, 500, 4) 1204 concatenate[0][0]
==================================================================================================
Total params: 2,008,405
Trainable params: 2,008,405
Non-trainable params: 0
__________________________________________________________________________________________________
完整代码
def MultiInputAndOutpt():
max_features = 10000
sequnce_length = 500
embedding_dims = 100
num_departments = 4
num_tags = 12
str = "hello"
title_vect = TextVectorization(max_tokens=max_features, output_mode="int", output_sequence_length=sequnce_length)
body_vect = TextVectorization(max_tokens=max_features, output_mode="int", output_sequence_length=sequnce_length)
title_input = keras.Input(shape=(1,), dtype=tf.string, name="title_input")
x1 = title_vect(title_input)
x1 = layers.Embedding(input_dim=max_features + 1, output_dim=embedding_dims)(x1)
body_input = keras.Input(shape=(1,), dtype=tf.string, name="body_input")
x2 = body_vect(body_input)
x2 = layers.Embedding(input_dim=max_features + 1, output_dim=embedding_dims)(x2)
tags_input = keras.Input(shape=(num_tags,), name="tags_input")
x3 = layers.Flatten()(tags_input)
x3 = layers.Dense(500)(x3)
x3 = tf.expand_dims(x3, axis=-1)
x3 = layers.Dense(100)(x3)
x = layers.concatenate([x1, x2, x3])
priority_score = layers.Dense(1)(x)
priority_score = tf.reshape(priority_score, (-1, 1), name="priority")
departments = layers.Dense(num_departments)(x)
departments = tf.reshape(departments, (-1, num_departments), name="departments")
model = keras.Model(inputs=[title_input, body_input, tags_input], outputs=[priority_score, departments])
model.summary()
model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.1),
loss=[keras.losses.BinaryCrossentropy(from_logits=True),
keras.losses.CategoricalCrossentropy(from_logits=True)],
loss_weights=[1.0, 0.2],
)
# title_data = np.random.randint(num_words, size=(1280, 10))
# body_data = np.random.randint(num_words, size=(1280, 100))
alphabet = np.array(list(string.ascii_lowercase + ' '))
title_data = np.random.choice(alphabet, size=(800, 1000))
body_data = np.random.choice(alphabet, size=(800, 1000))
tags_data = np.random.randint(2, size=(800, num_tags)).astype("float32")
body_data = ["".join(body_data[i]) for i in range(len(body_data))]
title_data = ["".join(title_data[i]) for i in range(len(title_data))]
# Dummy target data
priority_targets = np.random.random(size=(800, 1))
dept_targets = np.random.randint(2, size=(800, num_departments))
model.fit(
{"title_input": title_data, "body_input": body_data, "input3": tags_data},
{"priority": priority_targets, "departments": dept_targets},
epochs=2,
batch_size=32, )
【问题讨论】：
标签： numpy tensorflow keras