无法广播numpy数组，但是.shape表示它们具有相同的形状

Question

Here's the first part of my function that wants to generate text from a trained LSTM and a word embedding of dim 50. The problem comes when I try to set row i of X equal to the embedding vector y_embed. However, that problem only comes up on the third iteration of the for loop. That's strange to me, because I'd expect every row of X to have the same shape.

def generate_text(my_model, length):
    ix = np.random.randint(VOCAB_SIZE) #start generating by some 
         random index
    y_word = [reverse_dictionary[ix]] #get the word with that index
    y_embed = w2vec[ix] #get the embedding vector
    print(y_embed.shape)

    X = np.zeros((1, length, EMBED_DIM)) #make our numpy array
    print(X[0,2].shape)
    for i in range(length): #however many words we want
        print("i is "+str(i))
        X[0, i] = y_embed #current row of X is current word embedding
        y_embed = my_model.predict(X[:, :i+1, :])[0] 
        #input what we've generated so far, model.predict gives us a list, take the first one
        #we'll add it to our input on the next loop iteration

        y_word.append(vec2w(y_embed)) #lookup the word by its embedding

The for loop works for its first two iterations, and then throws this error when i=2:

 X[0, i] = y_embed #current row of X is current word embedding  
 ValueError: could not broadcast input array from shape (2,50) into shape (50)

So that's why I have it print the shape of y_embed and X[0,2] beforehand, and the console prints:

(50,)

(50,)

So as far as I can tell, they DO have the same shape. I'm still pretty new to numpy, so maybe it's something obvious, but I can't figure this one out. I should add that I'm using Keras, and model.predict expects a 3D tensor, which is why X is defined the way it is. I also tried setting X[0,i,:] = y_embed but that produced the same error at the same time.

Answer 1

X = np.zeros((1, length, EMBED_DIM))

X is 3d.

X[0, i]

selects on first 2 dir, so it is (EMBED_DIM,), which according to the error is (50,).

The error thinks y_embed is (2,50), 2 columns of 50. Apparently it was created by the last iteration.

my_model.predict(X[:, :i+1, :])[0] 

With i==1 , it is giving predict X[:,:2,:] , a (2,50) array. I don't know what predict does, but I don't think it's a coincidence that the output has the same shape as the input.