CODEWITHSUNDEEP
recurrent-neural-networkevaluatemlpsgd
PhamQuangTrung
PhamQuangTrung

Reputation: 31

expected axis -1 of input shape to have value 20 but received input with shape (None, 29)

ValueError: Input 0 of layer sequential_66 is incompatible with the layer: expected axis -1 of input shape to have value 20 but received input with shape (None, 29)
import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from keras.models import Sequential from keras.layers import Dense, Dropout, Activation from keras.optimizers import SGD

# Generate dummy data
import numpy as np
x_train = np.random.random((1000, 29))
y_train = keras.utils.to_categorical(np.random.randint(10, size=(1000, 1)), num_classes=10)
x_test = np.random.random((100, 20))
y_test = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)

model = Sequential()
# Dense(64) is a fully-connected layer with 64 hidden units.
# in the first layer, you must specify the expected input data shape:
# here, 20-dimensional vectors.
model.add(Dense(64, activation='relu', input_dim=20))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])

model.fit(x_train, y_train,
          epochs=20,
          batch_size=128)
score = model.evaluate(x_test, y_test, batch_size=128)

please, explained for me! Thanks.

Upvotes: 0

Views: 409

Answers (1)

PhamQuangTrung
PhamQuangTrung

Reputation: 31

learning:

#x_train, và x_test có dạng 2 chiều nên số cột của x_train là số chiều vào cho mạng ở trên.

Upvotes: 0

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