ValueError: Error when checking input: expected dense_1_input to have shape (24,) but got array with shape (1,)

Question

I'm trying to make a prediction with my model, the shape of the array that I am passing in shows as (24,) when printed. When trying to pass in the array into the predict method, it generates this error: ValueError: Error when checking input: expected dense_1_input to have shape (24,) but got array with shape (1,), however i know that the shape of my array is (24,). Why is it still giving an error?

for reference, here is my model:

model = MySequential()
model.add(Dense(units=128, activation='relu', input_shape=(24,)))
model.add(Dense(128, activation='relu'))
model.add(Dense(action_size, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

and that MySequential class is here, it is a subclass of keras.models.Sequential:

class MySequential(Sequential):
    score = 0
    def set_score(self, score):
        self.score = score
    def get_score(self):
        return self.score

the loop I'am running it in:

for i in range(100):
    new_model = create_model(action_size)
    new_model.__class__ = Sequential
    reward = 0
    state = env.reset()
    while True:
        env.render()
        print(state.shape)
        input_arr = state
        input_arr = np.reshape(input_arr, (1, 24))
        action = new_model.predict(input_arr)
        state, reward, done, info = env.step(action)
        if done:
            break
    env.reset()

Here is the full error-stack

Traceback (most recent call last):
  File "BipedalWalker.py", line 79, in <module>
    state, reward, done, info = env.step(action)
  File "/Users/arjunbemarkar/Python/MachineLearning/gym/gym/wrappers/time_limit.py", line 31, in step
    observation, reward, done, info = self.env.step(action)
  File "/Users/arjunbemarkar/Python/MachineLearning/gym/gym/envs/box2d/bipedal_walker.py", line 385, in step
    self.joints[0].motorSpeed     = float(SPEED_HIP     * np.sign(action[0]))
TypeError: only size-1 arrays can be converted to Python scalars

today · Accepted Answer · 2019-04-13 00:07:29Z

3

The input_shape argument specifies the input shape of one of the samples. So when you set it as (24,) it means each of your input samples has a shape of (24,). But you must consider that the models get batches of samples as their input. Therefore, their input shape is of the form (num_samples, ...). Since you want to feed your model with only one sample, your input array must have a shape of (1, 24). So you need to reshape your current array or add a new axis to the beginning:

import numpy as np

# either reshape it
input_arr = np.reshape(input_arr, (1, 24))

# or add a new axis to the beginning
input_arr = np.expand_dims(input_arr, axis=0)

# then call the predict method
preds = model.predict(input_arr)  # Note that the `preds` would have a shape of `(1, action_size)`

answered Apr 13, 2019 at 0:07

today

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8 Comments

Arjun Over a year ago

this doesn't work it gives TypeError: only size-1 arrays can be converted to Python scalars

today Over a year ago

@ArjunBemarkar It's hard to tell what's wrong without seeing your code. Could you please edit your question and add your code for this part to the end of question as an update?

today Over a year ago

@ArjunBemarkar Are you sure state is a numpy array?

Arjun Over a year ago

i tried input_arr = np.array(state), however that did not work for me

today Over a year ago

@ArjunBemarkar This is a bit strange! Could you please also add the full stack trace (i.e. error log) by editing your question?

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