前馈神经网络二分类测试

深度学习笔记/前馈神经网络/feedforward_neural_network.py

@@ -1,4 +1,5 @@
-import numpy as np 
+import numpy as np
+from keras.datasets import imdb
 
 def sigmoid(z):
     '''
@@ -105,4 +106,21 @@ def predict(X_new, parameters, threshold=0.5):
         else:
             activations[l] = sigmoid(prev_activations[l])
     prediction = (activations[L] > threshold).astype("int")
-    return prediction
+    return prediction
+
+def vectorize_sequences(sequences, dimension=10000):
+   
+    results = np.zeros((len(sequences), dimension))
+    for i, sequence in enumerate(sequences):
+        results[i, sequence] = 1.  # 索引results矩阵中的位置，赋值为1，全部都是从第0行0列开始的
+    return results
+
+(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
+
+# Our vectorized training data
+x_train = vectorize_sequences(train_data)
+# Our vectorized test data
+x_test = vectorize_sequences(test_data)
+
+y_train = np.asarray(train_labels).astype('float32')
+y_test = np.asarray(test_labels).astype('float32')