Hey Guys,

I hope you are getting a beautiful day. I’m stuck with this error: `TensorFlow.python.framework.errors_impl.InvalidArgumentError: Specified a list with shape [32,7] from a tensor with shape [17,7]`

I can’t even understand what’s going on. I’ll leave my code down here and, hopefully, someone can help me in finding a solution (please run the code to have a look, it is pretty simple):

```
from turtle import shape
import urllib
import zipfile
import pandas as pd
import tensorflow as tf
import numpy as np
def download_and_extract_data():
url = 'https://storage.googleapis.com/download.tensorflow.org/data/certificate/household_power.zip'
urllib.request.urlretrieve(url, 'household_power.zip')
with zipfile.ZipFile('household_power.zip', 'r') as zip_ref:
zip_ref.extractall()
def normalize_series(data, min, max):
data = data - min
data = data / max
return data
def windowed_dataset(series, batch_size, n_past=24, n_future=24, shift=1):
ds = tf.data.Dataset.from_tensor_slices(series)
ds = ds.window(size=n_past + n_future, shift=shift, drop_remainder=True)
ds = ds.flat_map(lambda w: w.batch(n_past + n_future))
ds = ds.map(lambda w: (w[:n_past], w[n_past:]))
return ds.batch(batch_size).prefetch(1)
df = pd.read_csv('household_power_consumption.csv', sep=',',
infer_datetime_format=True, index_col='datetime', header=0)
N_FEATURES = len(df.columns) # DO NOT CHANGE THIS
data = df.values
data = normalize_series(data, data.min(axis=0), data.max(axis=0))
SPLIT_TIME = int(len(data) * 0.5) # DO NOT CHANGE THIS
x_train = data[:SPLIT_TIME]
x_valid = data[SPLIT_TIME:]
tf.keras.backend.clear_session()
tf.random.set_seed(42)
BATCH_SIZE = 32 # ADVISED NOT TO CHANGE THIS
N_PAST = 24 # DO NOT CHANGE THIS
N_FUTURE = 24 # DO NOT CHANGE THIS
SHIFT = 1 # DO NOT CHANGE THIS
train_set = windowed_dataset(series=x_train, batch_size=BATCH_SIZE,
n_past=N_PAST, n_future=N_FUTURE,
shift=SHIFT)
valid_set = windowed_dataset(series=x_valid, batch_size=BATCH_SIZE,
n_past=N_PAST, n_future=N_FUTURE,
shift=SHIFT)
model = tf.keras.models.Sequential([
# ADD YOUR LAYERS HERE.
# If you don't follow the instructions in the following comments,
# tests will fail to grade your code:
# The input layer of your model must have an input shape of:
# (BATCH_SIZE, N_PAST = 24, N_FEATURES = 7)
# The model must have an output shape of:
# (BATCH_SIZE, N_FUTURE = 24, N_FEATURES = 7).
# Make sure that there are N_FEATURES = 7 neurons in the final dense
# layer since the model predicts 7 features.
# HINT: Bidirectional LSTMs may help boost your score. This is only a
# suggestion.
# WARNING: If you are using the GRU layer, it is advised not to use the
# recurrent_dropout argument (you can alternatively set it to 0),
# since it has not been implemented in the cuDNN kernel and may
# result in much longer training times.
tf.keras.layers.InputLayer(input_shape=(N_PAST, N_FEATURES), batch_size = BATCH_SIZE, name='Input'),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(N_FEATURES, activation='relu', return_sequences=True)),
tf.keras.layers.Dense(N_FEATURES)
])
optimizer = tf.keras.optimizers.Adam()
model.summary()
model.compile(
optimizer=optimizer,
loss='mae'
)
model.fit(
train_set,
epochs = 5,
validation_data = valid_set,
steps_per_epoch = len(list(train_set.as_numpy_iterator())),
validation_steps = len(list(valid_set.as_numpy_iterator())),
batch_size =1
```