Keras AttributeError: 'list' object has no attribute 'ndim'
I don't know the shape of your training data but I suspect that you have an error on your input_dim
. Try changing it to input_dim=len(X_data)
like this:
model = Sequential()
model.add(Dense(5, input_dim=len(X_data), activation='sigmoid' ))
model.add(Dense(1, activation = 'sigmoid'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['acc'])
model.fit(X_data, y_data, epochs=20, batch_size=10)
model.fit
expects x and y to be numpy array. Seems like you pass a list, it tried to get shape of input by reading ndim
attribute of numpy array and failed.
You can simply transform it using np.array
:
import numpy as np
...
model.fit(np.array(train_X),np.array(train_Y), epochs=20, batch_size=10)
When you import you should use tensorflow.keras
instead of just keras
like this:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Conv2D, MaxPool2D, Dense
because there is a bug related to the keras
module.
Reference: here.