I'm trying to train LSTM network on data taken from a DataFrame.
Here's the code:
x_lstm=x.to_numpy().reshape(1,x.shape[0],x.shape[1])
model = keras.models.Sequential([
keras.layers.LSTM(x.shape[1], return_sequences=True, input_shape=(x_lstm.shape[1],x_lstm.shape[2])),
keras.layers.LSTM(NORMAL_LAYER_SIZE, return_sequences=True),
keras.layers.LSTM(NORMAL_LAYER_SIZE),
keras.layers.Dense(y.shape[1])
])
optimizer=keras.optimizers.Adadelta()
model.compile(loss="mse", optimizer=optimizer)
for i in range(150):
history = model.fit(x_lstm, y)
save_model(model,'tmp.rnn')
This fails with
ValueError: Data cardinality is ambiguous:
x sizes: 1
y sizes: 99
Please provide data which shares the same first dimension.
When I change model to
model = keras.models.Sequential([
keras.layers.LSTM(x.shape[1], return_sequences=True, input_shape=x_lstm.shape),
keras.layers.LSTM(NORMAL_LAYER_SIZE, return_sequences=True),
keras.layers.LSTM(NORMAL_LAYER_SIZE),
keras.layers.Dense(y.shape[1])
])
it fails with following error:
Input 0 of layer lstm_9 is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, 1, 99, 1200]
How do I get this to work?
x has shape of (99, 1200) (99 items with 1200 features each, this is just sample a larger dataset), y has shape (99, 1)
1 Answer
As the Error suggests, the First Dimension of X and y is different. First Dimension indicates the Batch Size and it should be same.
Please ensure that Y also has the shape, (1, something).
I could reproduce your error with the Code shown below:
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import tensorflow as tf
import numpy as np
# define sequences
sequences = [
[1, 2, 3, 4],
[1, 2, 3],
[1]
]
# pad sequence
padded = pad_sequences(sequences)
X = np.expand_dims(padded, axis = 0)
print(X.shape) # (1, 3, 4)
y = np.array([1,0,1])
#y = y.reshape(1,-1)
print(y.shape) # (3,)
model = Sequential()
model.add(LSTM(4, return_sequences=False, input_shape=(None, X.shape[2])))
model.add(Dense(1, activation='sigmoid'))
model.compile (
loss='mean_squared_error',
optimizer=tf.keras.optimizers.Adam(0.001))
model.fit(x = X, y = y)
If we observe the Print Statements,
Shape of X is (1, 3, 4)
Shape of y is (3,)
This Error can be fixed by uncommenting the Line, y = y.reshape(1,-1), which makes the First Dimension (Batch_Size) equal (1) for both X and y.
Now, the working code is shown below, along with the Output:
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import tensorflow as tf
import numpy as np
# define sequences
sequences = [
[1, 2, 3, 4],
[1, 2, 3],
[1]
]
# pad sequence
padded = pad_sequences(sequences)
X = np.expand_dims(padded, axis = 0)
print('Shape of X is ', X.shape) # (1, 3, 4)
y = np.array([1,0,1])
y = y.reshape(1,-1)
print('Shape of y is', y.shape) # (1, 3)
model = Sequential()
model.add(LSTM(4, return_sequences=False, input_shape=(None, X.shape[2])))
model.add(Dense(1, activation='sigmoid'))
model.compile (
loss='mean_squared_error',
optimizer=tf.keras.optimizers.Adam(0.001))
model.fit(x = X, y = y)
The Output of above code is :
Shape of X is (1, 3, 4)
Shape of y is (1, 3)
1/1 [==============================] - 0s 1ms/step - loss: 0.2588
<tensorflow.python.keras.callbacks.History at 0x7f5b0d78f4a8>
Hope this helps. Happy Learning!
