python - ValueError:检查目标时出错:预期 main_prediction 有 3 个维度，但得到形状为 (1128, 1) 的数组

Question

我正在尝试将使用Python2.7和Keras 1.x的旧代码改编为Python3.7.3、Keras 2.2.4和TensorFlow 1.13.1。代码如下:

from keras.layers import Input, add, Dense, Flatten, concatenate
from keras import activations
from keras import models
from keras import backend as K
import numpy as np

import utils
from NGF.preprocessing import tensorise_smiles, tensorise_smiles_mp
from NGF.layers import NeuralGraphHidden, NeuralGraphOutput
from NGF.models import build_graph_conv_model
from NGF.sparse import GraphTensor, EpochIterator

# ==============================================================================
# ================================ Load the data ===============================
# ==============================================================================
print("{:=^100}".format(' Data preprocessing '))
data, labels = utils.load_delaney()

# Tensorise data
X_atoms, X_bonds, X_edges = tensorise_smiles_mp(data)
print('Atoms:', X_atoms.shape)
print('Bonds:', X_bonds.shape)
print('Edges:', X_edges.shape)

# Load sizes from data shape
num_molecules = X_atoms.shape[0]
max_atoms = X_atoms.shape[1]
max_degree = X_bonds.shape[2]
num_atom_features = X_atoms.shape[-1]
num_bond_features = X_bonds.shape[-1]

# ==============================================================================
# =============== Example 1: Building a 3-layer graph convnet  =================
# ==============================================================================
print("{:=^100}".format(' Example 1 '))

# Parameters
conv_width = 8
fp_length = 62

# Define the input layers
atoms0 = Input(name='atom_inputs', shape=(max_atoms, num_atom_features))
bonds = Input(name='bond_inputs', shape=(max_atoms, max_degree, num_bond_features))
edges = Input(name='edge_inputs', shape=(max_atoms, max_degree), dtype='int32')
print("DEBUG: edges=", K.print_tensor(edges))

# Define the convoluted atom feature layers
atoms1 = NeuralGraphHidden(conv_width, activation='relu', use_bias=False)([atoms0, bonds, edges])
atoms2 = NeuralGraphHidden(conv_width, activation='relu', use_bias=False)([atoms1, bonds, edges])

# Define the outputs of each (convoluted) atom feature layer to fingerprint
fp_out0 = NeuralGraphOutput(fp_length, activation='softmax')([atoms0, bonds, edges])
fp_out1 = NeuralGraphOutput(fp_length, activation='softmax')([atoms1, bonds, edges])
fp_out2 = NeuralGraphOutput(fp_length, activation='softmax')([atoms2, bonds, edges])

# Flatten the input before the Dense layer by summing the 3 outputs to obtain fingerprint
# final_fp = merge([fp_out0, fp_out1, fp_out2], mode='sum') # Old Keras 1.x syntax
print("DEBUG: fp_out0.get_shape()=", fp_out0.get_shape())
print("DEBUG: fp_out1.get_shape()=", fp_out1.get_shape())
print("DEBUG: fp_out2.get_shape()=", fp_out2.get_shape())
# final_fp = add([fp_out0, fp_out1, fp_out2])
final_fp = concatenate([fp_out0, fp_out1, fp_out2])
print("DEBUG: final_fp.get_shape()=", final_fp.get_shape())

# Build and compile model for regression.
main_pred = Dense(1, activation='linear', name='main_prediction')(final_fp)
print("DEBUG: main_pred.get_shape()=", main_pred.get_shape())
model = models.Model(inputs=[atoms0, bonds, edges], outputs=[main_pred])
model.compile(optimizer='adagrad', loss='mse')

# Show summary
model.summary()

# Train the model
print("DEBUG: labels.shape", labels.shape)
model.fit(x=[X_atoms, X_bonds, X_edges], y=labels, epochs=20, batch_size=32, validation_split=0.2)

本质上，它是一个定制的卷积神经网络，它采用 3 个不同的可变维度数组作为输入并返回标量预测。这是我执行时的输出:

======================================== Data preprocessing ========================================
Tensorising molecules in batches...
1128/1128 [==================================================] - 1s 740us/step
Merging batch tensors...    [DONE]
Atoms: (1128, 55, 62)
Bonds: (1128, 55, 5, 6)
Edges: (1128, 55, 5)
============================================ Example 1 =============================================
DEBUG: edges= Tensor("Print:0", shape=(?, 55, 5), dtype=int32)
DEBUG: fp_out0.get_shape()= (?, 62)
DEBUG: fp_out1.get_shape()= (?, 62)
DEBUG: fp_out2.get_shape()= (?, 62)
DEBUG: final_fp.get_shape()= (?, 186)
DEBUG: main_pred.get_shape()= (?, 1)
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
atom_inputs (InputLayer)        (None, 55, 62)       0                                            
__________________________________________________________________________________________________
bond_inputs (InputLayer)        (None, 55, 5, 6)     0                                            
__________________________________________________________________________________________________
edge_inputs (InputLayer)        (None, 55, 5)        0                                            
__________________________________________________________________________________________________
neural_graph_hidden_1 (NeuralGr [(None, 55, 62), (No 2720        atom_inputs[0][0]                
                                                                 bond_inputs[0][0]                
                                                                 edge_inputs[0][0]                
__________________________________________________________________________________________________
neural_graph_hidden_2 (NeuralGr [(None, 55, 62), (No 2720        neural_graph_hidden_1[0][0]      
                                                                 bond_inputs[0][0]                
                                                                 edge_inputs[0][0]                
__________________________________________________________________________________________________
neural_graph_output_1 (NeuralGr [(None, 55, 62), (No 4278        atom_inputs[0][0]                
                                                                 bond_inputs[0][0]                
                                                                 edge_inputs[0][0]                
__________________________________________________________________________________________________
neural_graph_output_2 (NeuralGr [(None, 55, 62), (No 4278        neural_graph_hidden_1[0][0]      
                                                                 bond_inputs[0][0]                
                                                                 edge_inputs[0][0]                
__________________________________________________________________________________________________
neural_graph_output_3 (NeuralGr [(None, 55, 62), (No 4278        neural_graph_hidden_2[0][0]      
                                                                 bond_inputs[0][0]                
                                                                 edge_inputs[0][0]                
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 55, 186)      0           neural_graph_output_1[0][0]      
                                                                 neural_graph_output_2[0][0]      
                                                                 neural_graph_output_3[0][0]      
__________________________________________________________________________________________________
main_prediction (Dense)         (None, 55, 1)        187         concatenate_1[0][0]              
==================================================================================================
Total params: 18,461
Trainable params: 18,461
Non-trainable params: 0
__________________________________________________________________________________________________
DEBUG: labels.shape (1128,)
Traceback (most recent call last):
  File "/home/thomas/Programs/Anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3296, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-2-9a41784534dc>", line 1, in <module>
    runfile('/home2/thomas/Programs/keras-neural-graph-fingerprint_Py3/examples.py', wdir='/home2/thomas/Programs/keras-neural-graph-fingerprint_Py3')
  File "/home2/thomas/Programs/pycharm-2019.1.1/helpers/pydev/_pydev_bundle/pydev_umd.py", line 197, in runfile
    pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script
  File "/home2/thomas/Programs/pycharm-2019.1.1/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "/home2/thomas/Programs/keras-neural-graph-fingerprint_Py3/examples.py", line 80, in <module>
    model.fit(x=[X_atoms, X_bonds, X_edges], y=labels, epochs=20, batch_size=32, validation_split=0.2)
  File "/home/thomas/Programs/Anaconda3/lib/python3.7/site-packages/keras/engine/training.py", line 952, in fit
    batch_size=batch_size)
  File "/home/thomas/Programs/Anaconda3/lib/python3.7/site-packages/keras/engine/training.py", line 789, in _standardize_user_data
    exception_prefix='target')
  File "/home/thomas/Programs/Anaconda3/lib/python3.7/site-packages/keras/engine/training_utils.py", line 128, in standardize_input_data
    'with shape ' + str(data_shape))
ValueError: Error when checking target: expected main_prediction to have 3 dimensions, but got array with shape (1128, 1)

我怀疑此错误与“标签”数组的形状有关，该数组是扁平的。我究竟做错了什么？另外，为什么我会得到

DEBUG: final_fp.get_shape()= (?, 186)

但是 model.summary() 显示

concatenate_1 (Concatenate) (None, 55, 186) 0

这个额外的维度 (55) 从哪里来？也许网络出于某种原因期望标签具有尺寸 (1128, 55, 1)而不是(1128, 1) .

如果您需要更多信息，请询问我，我将添加更多调试打印功能。

Answer 1

你的最后一个密集层main_predictions没有给出二维输出，因为你没有压平它的输入。

You need to use a Flatten layer after Convolution layers so that the output of the Dense is 2 dimensional.

main_predictions 需要 3D 标签，但您为其提供 2D 标签。因此，您会收到错误。

您可以在代码中添加 Flatten 层，如下所示:

flatten = Flatten()( final_fp )
main_pred = Dense(1, activation='linear', name='main_prediction')( flatten )

然后编译模型。