用Keras预测因变量对整个数组显示相同的结果，并且混淆矩阵抛出错误

Question

I am building a ANN with Keras. The shape of my df is (120000,18). My goal is to predict based on 17 independent variables(X's) what my dependent variable(Y) will be. I have 2 questions which I added below. Here is my code:

Creating ANN

Question 1: How can all my values for y_pred_train for my Training set be the same value? Also, the predictions should show a binary result, meaning 0 or 1, meaning if prediction will be true vs false. Why am I getting 0.41542563?

Data Preprocessing
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

#ANN
import keras
from keras.models import Sequential
from keras.layers import Dense

# Initialising the ANN
classifier = Sequential()
# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'relu', input_dim = 17))
# Adding the second hidden layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'relu'))
# Adding the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)

Snippet of epoch at point of convergence: 7210/80030 [=>............................] - ETA: 8s - loss: 0.6822 - acc: 0.7046

# Classifying the Train set results
y_pred_train = classifier.predict(X_train)
y_pred_train

Out[50]: 
array([[0.41542563],
       [0.41542563],
       [0.41542563],
       ...,
       [0.41542563],
       [0.41542563],
       [0.41542563]], dtype=float32)

Creating the Confusion Matrix

Question 2: When I try to execute a Confusion matrix, I get an error.

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

ValueError-Traceback (most recent call last)
<command-207679> in <module>()
      1 # Making the Confusion Matrix
      2 from sklearn.metrics import confusion_matrix
----> 3 cm = confusion_matrix(y_test, y_pred)

/databricks/python/lib/python3.5/site-packages/sklearn/metrics/classification.py in confusion_matrix(y_true, y_pred, labels, sample_weight)
    238 
    239     """
--> 240     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
    241     if y_type not in ("binary", "multiclass"):
    242         raise ValueError("%s is not supported" % y_type)

/databricks/python/lib/python3.5/site-packages/sklearn/metrics/classification.py in _check_targets(y_true, y_pred)
     70     y_pred : array or indicator matrix
     71     """
---> 72     check_consistent_length(y_true, y_pred)
     73     type_true = type_of_target(y_true)
     74     type_pred = type_of_target(y_pred)

/databricks/python/lib/python3.5/site-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
    179     if len(uniques) > 1:
    180         raise ValueError("Found input variables with inconsistent numbers of"
--> 181                          " samples: %r" % [int(l) for l in lengths])
    182 
    183 

ValueError: Found input variables with inconsistent numbers of samples: [34299, 22866]

Answer 1

Your dense layers only have 1 unit each. Except the first layer, rest of the layers are just passing the values along. Hence, all the outputs are same.

As for your second question, the activation function on the last layer is sigmoid, which gives an output in the range of 0-1 . You will have to convert them based on a threshold. For example,

y_pred = [1 if x>0.5 else 0 for x in y_pred] , in which 0.5 is the threshold.