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pythonpandastensorflowkeraslstm

LSTM: Input 0 of layer sequential is incompatible with the layer

I know there are several questions about this here, but I haven't found one which fits exactly my problem. I'm trying to fit an LSTM with data from Pandas DataFrames but getting confused about the format I have to provide them. I created a small code snipped which shall show you what I try to do:

import pandas as pd, tensorflow as tf, random
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

targets = pd.DataFrame(index=pd.date_range(start='2019-01-01', periods=300, freq='D'))
targets['A'] = [random.random() for _ in range(len(targets))]
targets['B'] = [random.random() for _ in range(len(targets))] 
features = pd.DataFrame(index=targets.index)
for i in range(len(features)) :
    features[str(i)] = [random.random() for _ in range(len(features))] 

model = Sequential()
model.add(LSTM(units=targets.shape[1], input_shape=features.shape))
model.compile(optimizer='adam', loss='mae')

model.fit(features, targets, batch_size=10, epochs=10)

this results to:

ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: [10, 300]

which I expect relates to the dimensions of the features DataFrame provided. I guess that once fixed this the next error would mention the targets DataFrame.

As far as I understand, 'units' parameter of my first layer defines the output dimensionality of this model. The inputs have to have a 3D shape, but I don't know how to create them out of the 2D world of the Data Frames. I hope you can help me understanding the reshape mechanism in Python and how to use them in combination with Pandas DataFrames. (I'm quite new to Python and came from R)

Thankls in advance

Solution

  • Lets looks at the few popular ways in LSTMs are used.

    Many to Many

    Example: You have a sentence (composed of words in sequence). Give these sequence of words you would like to predict the Parts of speech (POS) of each word.

    enter image description here

    So you have n words and you feed each word per timestep to the LSTM. Each LSTM timestep (also called LSTM unwrapping) will produce and output. The word is represented by a a set of features normally word embeddings. So the input to LSTM is of size bath_size X time_steps X features

    Keras code:

    inputs = keras.Input(shape=(10,3))
lstm = keras.layers.LSTM(8, input_shape = (10, 3), return_sequences = True)(inputs)
outputs = keras.layers.TimeDistributed(keras.layers.Dense(5, activation='softmax'))(lstm)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss='categorical_crossentropy', optimizer='adam')

X = np.random.randn(4,10,3) 
y = np.random.randint(0,2, size=(4,10,5))

model.fit(X, y, epochs=2)
print (model.predict(X).shape)

    Many to One

    Example: Again you have a sentence (composed of words in sequence). Give these sequence of words you would like to predict sentiment of the sentence if it is positive or negative.

    enter image description here

    Keras code

    inputs = keras.Input(shape=(10,3))
lstm = keras.layers.LSTM(8, input_shape = (10, 3), return_sequences = False)(inputs)
outputs =keras.layers.Dense(5, activation='softmax')(lstm)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss='categorical_crossentropy', optimizer='adam')

X = np.random.randn(4,10,3) 
y = np.random.randint(0,2, size=(4,5))

model.fit(X, y, epochs=2)
print (model.predict(X).shape)

    Many to multi-headed

    Example: You have a sentence (composed of words in sequence). Give these sequence of words you would like to predict sentiment of the sentence as well the author of the sentence.

    This is multi-headed model where one head will predict the sentiment and another head will predict the author. Both the heads share the same LSTM backbone.

    enter image description here

    Keras code

    inputs = keras.Input(shape=(10,3))
lstm = keras.layers.LSTM(8, input_shape = (10, 3), return_sequences = False)(inputs)
output_A = keras.layers.Dense(5, activation='softmax')(lstm)
output_B = keras.layers.Dense(5, activation='softmax')(lstm)

model = keras.Model(inputs=inputs, outputs=[output_A, output_B])
model.compile(loss='categorical_crossentropy', optimizer='adam')

X = np.random.randn(4,10,3) 
y_A = np.random.randint(0,2, size=(4,5))
y_B = np.random.randint(0,2, size=(4,5))

model.fit(X, [y_A, y_B], epochs=2)
y_hat_A, y_hat_B = model.predict(X)
print (y_hat_A.shape, y_hat_B.shape)

    What you are looking for is Many to Multi head model where your predictions for A will be made by one head and another head will make predictions for B