CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasneural-network
Arjun
Arjun

Reputation: 398

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

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

Upvotes: 2

Views: 2703

Answers (1)

today
today

Reputation: 33460

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)`

Upvotes: 3

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