我如何克服这个问题，模型适合 python

Question

我正在研究机器学习。很有趣！

我有一个关于错误的问题。 我在下面分享代码和错误消息。 请解决它..！非常感谢！ 如果sequential_4 ...，则错误显示值错误输入0层...

a=df4['age']
b=df4['growth']
    
X=np.array(a.values.tolist())
y=np.array(b.values.tolist())

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split
import numpy
import tensorflow as tf

seed = 0
numpy.random.seed(seed)
tf.random.set_seed(3)

X_train, X_test, y_train, y_test = train_test_split(a, b,
                                                    test_size = 0.3, random_state=seed)

model = Sequential()
model.add(Dense(30, input_dim=17, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1))

model.compile(loss='mean_sqaured_error',
              optimizer='adam')

model.fit(X_train, y_train, validation_data= (X_test, y_test), epochs=200, batch_size=10)
 

错误信息 纪元 1/200

ValueError                                Traceback (most recent call last)
<ipython-input-56-ffc8e137fb64> in <module>()
----> 1 model.fit(X_train, y_train, validation_data= (X_test, y_test), epochs=200, batch_size=10)

9 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    975           except Exception as e:  # pylint:disable=broad-except
    976             if hasattr(e, "ag_error_metadata"):
--> 977               raise e.ag_error_metadata.to_exception(e)
    978             else:
    979               raise

ValueError：在用户代码中：

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
    return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
    return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step  **
    outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:754 train_step
    y_pred = self(x, training=True)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:998 __call__
    input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:259 assert_input_compatibility
    ' but received input with shape ' + display_shape(x.shape))

ValueError: Input 0 of layer sequential_14 is incompatible with the layer: expected axis -1 of input shape to have value 17 but received input with shape (None, 1)

Answer 1

我能够使用下面显示的示例代码复制您的问题

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split

X = np.random.random((1000,1))
y = np.random.random((1000,1))
 
X_train,X_test, y_train,y_test = train_test_split(X,y)
 
dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
train_data = dataset.shuffle(len(X_train)).batch(32)
train_data = train_data.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
 
valid_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test))

model = Sequential()
model.add(Dense(30, input_dim=17, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1))

model.compile(loss='mean_sqaured_error',
              optimizer='adam')

model.fit(train_data, epochs=3, validation_data=valid_ds) 

输出：

Epoch 1/3
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-11-0e4d5121895c> in <module>()
     30 
     31 #model.fit(X_train, y_train, validation_data= (X_test, y_test), epochs=200, batch_size=10)
---> 32 model.fit(train_data, epochs=3, validation_data=valid_ds)

9 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    975           except Exception as e:  # pylint:disable=broad-except
    976             if hasattr(e, "ag_error_metadata"):
--> 977               raise e.ag_error_metadata.to_exception(e)
    978             else:
    979               raise

ValueError: in user code:

    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:754 train_step
        y_pred = self(x, training=True)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:998 __call__
        input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_spec.py:259 assert_input_compatibility
        ' but received input with shape ' + display_shape(x.shape))

    ValueError: Input 0 of layer sequential_2 is incompatible with the layer: expected axis -1 of input shape to have value 17 but received input with shape (None, 1)

固定代码：

这里你的顺序模型的输入层必须设置为1而不是17，因为你的输入数据的形状是(None, 1)。

您可以将损失函数指定为mse，而不是model.compile 中的mean_sqaured_error。

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split

X = np.random.random((1000,1))
y = np.random.random((1000,1))
 
X_train,X_test, y_train,y_test = train_test_split(X,y)
 
dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
train_data = dataset.shuffle(len(X_train)).batch(32)
train_data = train_data.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
 
valid_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test))

model = Sequential()
model.add(Dense(30, input_dim=1, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1))

model.compile(loss='mse',
              optimizer='adam')

model.fit(train_data, epochs=3, validation_data=valid_ds) 

输出：

Epoch 1/3
24/24 [==============================] - 1s 28ms/step - loss: 0.5050 - val_loss: 0.2758
Epoch 2/3
24/24 [==============================] - 0s 21ms/step - loss: 0.2704 - val_loss: 0.1908
Epoch 3/3
24/24 [==============================] - 0s 21ms/step - loss: 0.2047 - val_loss: 0.1454
<tensorflow.python.keras.callbacks.History at 0x7fc28239d2d0>