在Keras中构建简单的前馈神经网络时遇到关键错误

Question

Here is a snapshot of my dataset, including its shape:

Now, here is the code I am using to build the NN:

# define the architecture of the network
model = Sequential()
model.add(Dense(50, input_dim=X_train.shape[1], init="uniform", activation="relu"))
model.add(Dense(38, activation="relu", kernel_initializer="uniform"))
model.add(Dense(1, activation = 'sigmoid'))

print("[INFO] compiling model...")
adam = Adam(lr=0.01)
model.compile(loss="binary_crossentropy", optimizer=adam,
    metrics=["accuracy"])
model.fit(X_train, Y_train, epochs=50, batch_size=128,
    verbose=1)

When I do this, I get the following error:

KeyError: '[233946 164308 296688 166151 276165  88219 117980 163503 182033 164328\n 188083  30380  37984 245771 308534   6215 181186 307488 172375  60446\n  29397 166681   5587 243263 103579 262182 107823 234790 258973 116433\n 199283  86118 172148 257334 286452 248407  81280 ...] not in index'

I haven't been able to find a solution to this. Any help would be much appreciated.

Answer 1

I believe that the input is not a numpy array as described in this github issue on the keras page

Try fitting the model using this:

model.fit(np.array(X_train), np.array(Y_train), epochs=50, batch_size=128,
    verbose=1)

Which will cast the arrays as numpy arrays when fitting the data.