I know this is a dumb question but I cant seem to figure it out. I feed in a numpy array of (?,200,75) and get this error:
ValueError: Cannot feed value of shape (64, 200, 75) for Tensor 'TargetsData/Y:0', which has shape '(200, 75)'
Here is my code:
import numpy as np
import tflearn
print("loading features....")
features = np.load("features_xs.npy")
print("loading classes....")
classes = np.load("classes_xs.npy")
symbols = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p'
,'q','r','s','t','u','v','w','x','y','z',
'A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U',
'V','W','X','Y','Z','1','2','3','4','5','6','7','8','9','0','.',',',
'!','?',':',';','\'','(',')','-','_',' ','"',]
num_symbols = len(symbols)
input_layer = tflearn.input_data(shape=[None, 200,num_symbols])
input_layer = tflearn.flatten(input_layer)
dense1 = tflearn.fully_connected(input_layer, 1000, activation='tanh',
regularizer='L2', weight_decay=0.001)
dense2 = tflearn.fully_connected(dense1, 2000, activation='tanh',
regularizer='L2', weight_decay=0.001)
dense2 = tflearn.fully_connected(dense2, 1000, activation='tanh',
regularizer='L2', weight_decay=0.001)
dropout2 = tflearn.dropout(dense2, 0.8)
final = tflearn.fully_connected(dropout2, (200*num_symbols), activation='tanh')
reshape = tflearn.reshape(final, [200,num_symbols], name="Reshape")
Adam = tflearn.Adam(learning_rate=0.01)
net = tflearn.regression(reshape, optimizer=Adam,
loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net, tensorboard_verbose=0)
model.fit(features, classes, n_epoch=1, show_metric=True, run_id="dense_model")
model.save("model")
num_symbols is == to 75 in case you're wondering
I can't find the solution please help thanks.
Run the following code:
print(classes.shape)
You will be getting an output of (64, 200, 75). But your final layer reshape is expecting shape of (200, 75). You will have to supply values with shape of (200, 75) from your classes variable to resolve the error.