Ytukyg

I'm getting this error. I'm quite new to ML.

ValueError: Error when checking input: expected Sequence to have 3 dimensions, but got array with shape (500, 400)

These are the below codes that I'm using.

print(X1_Train.shape)

print(X2_Train.shape)

print(y_train.shape)



====================================

Output (here I've 500 rows in each):

(500, 400)

(500, 1500)

(500,)



400 => timesteps (below)

1500 => n (below)

====================================





timesteps = 50 * 8

n = 50 * 30



def createClassifier():

    sequence = Input(shape=(timesteps, 1), name='Sequence')

    features = Input(shape=(n,), name='Features')



    conv = Sequential()

    conv.add(Conv1D(10, 5, activation='relu', input_shape=(timesteps, 1)))

    conv.add(Conv1D(10, 5, activation='relu'))

    conv.add(MaxPool1D(2))

    conv.add(Dropout(0.5))



    conv.add(Conv1D(5, 6, activation='relu'))

    conv.add(Conv1D(5, 6, activation='relu'))

    conv.add(MaxPool1D(2))

    conv.add(Dropout(0.5))

    conv.add(Flatten())

    part1 = conv(sequence)



    merged = concatenate([part1, features])



    final = Dense(512, activation='relu')(merged)

    final = Dropout(0.5)(final)

    final = Dense(num_class, activation='softmax')(final)



    model = Model(inputs=[sequence, features], outputs=[final])

    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

    return model



model = createClassifier()

# print(model.summary())

history = model.fit([X1_Train, X2_Train], y_train, epochs =5)

Any insight please ?
Thanks in advance.

Two things -

Conv1D layer expects input to be in the shape (batch_size, x, filters), in your case (500,400,1).

You need to reshape your input layer, add another axis, of size 1. (this does not change anything in your data).

You are trying to use multiple inputs, Sequential API is not the best choice for that. I would recommend using the Functional API

Edit:
Regarding your comment, not sure what you did wrong, but this is a working version of your code (with fake data), with a reshape:

import keras



import numpy as np







X1_Train = np.ones((500,400))

X2_Train = np.ones((500,1500))

y_train = np.ones((500))

print(X1_Train.shape)

print(X2_Train.shape)

print(y_train.shape)



num_class = 1





timesteps = 50 * 8

n = 50 * 30



def createClassifier():

    sequence = keras.layers.Input(shape=(timesteps, 1), name='Sequence')

    features = keras.layers.Input(shape=(n,), name='Features')



    conv = keras.Sequential()

    conv.add(keras.layers.Conv1D(10, 5, activation='relu', input_shape=(timesteps, 1)))

    conv.add(keras.layers.Conv1D(10, 5, activation='relu'))

    conv.add(keras.layers.MaxPool1D(2))

    conv.add(keras.layers.Dropout(0.5))



    conv.add(keras.layers.Conv1D(5, 6, activation='relu'))

    conv.add(keras.layers.Conv1D(5, 6, activation='relu'))

    conv.add(keras.layers.MaxPool1D(2))

    conv.add(keras.layers.Dropout(0.5))

    conv.add(keras.layers.Flatten())

    part1 = conv(sequence)



    merged = keras.layers.concatenate([part1, features])



    final = keras.layers.Dense(512, activation='relu')(merged)

    final = keras.layers.Dropout(0.5)(final)

    final = keras.layers.Dense(num_class, activation='softmax')(final)



    model = keras.Model(inputs=[sequence, features], outputs=[final])

    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

    return model



model = createClassifier()

# print(model.summary())

X1_Train = X1_Train.reshape((500,400,1))

history = model.fit([X1_Train, X2_Train], y_train, epochs =5)

With output:

Using TensorFlow backend.

(500, 400)

(500, 1500)

(500,)

Epoch 1/5

500/500 [==============================] - 1s 3ms/step - loss: 1.1921e-07 - acc: 1.0000

Epoch 2/5

500/500 [==============================] - 0s 160us/step - loss: 1.1921e-07 - acc: 1.0000

Epoch 3/5

500/500 [==============================] - 0s 166us/step - loss: 1.1921e-07 - acc: 1.0000

Epoch 4/5

500/500 [==============================] - 0s 154us/step - loss: 1.1921e-07 - acc: 1.0000

Epoch 5/5

500/500 [==============================] - 0s 157us/step - loss: 1.1921e-07 - acc: 1.0000