Keras, how do I predict after I trained a model?
I'm playing with the reuters-example dataset and it runs fine (my model is trained). I read about how to save a model, so I could load it later to use again. But how do I use this saved model to predict a new text? Do I use models.predict()?
Do I have to prepare this text in a special way?
I tried it with
import keras.preprocessing.text
text = np.array(['this is just some random, stupid text'])
print(text.shape)
tk = keras.preprocessing.text.Tokenizer(
nb_words=2000,
filters=keras.preprocessing.text.base_filter(),
lower=True,
split=" ")
tk.fit_on_texts(text)
pred = tk.texts_to_sequences(text)
print(pred)
model.predict(pred)
But I always get
(1L,)
[[2, 4, 1, 6, 5, 7, 3]]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-83-42d744d811fb> in <module>()
7 print(pred)
8
----> 9 model.predict(pred)
C:\Users\bkey\Anaconda2\lib\site-packages\keras\models.pyc in predict(self, x, batch_size, verbose)
457 if self.model is None:
458 self.build()
--> 459 return self.model.predict(x, batch_size=batch_size, verbose=verbose)
460
461 def predict_on_batch(self, x):
C:\Users\bkey\Anaconda2\lib\site-packages\keras\engine\training.pyc in predict(self, x, batch_size, verbose)
1132 x = standardize_input_data(x, self.input_names,
1133 self.internal_input_shapes,
-> 1134 check_batch_dim=False)
1135 if self.stateful:
1136 if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0:
C:\Users\bkey\Anaconda2\lib\site-packages\keras\engine\training.pyc in standardize_input_data(data, names, shapes, check_batch_dim, exception_prefix)
79 for i in range(len(names)):
80 array = arrays[i]
---> 81 if len(array.shape) == 1:
82 array = np.expand_dims(array, 1)
83 arrays[i] = array
AttributeError: 'list' object has no attribute 'shape'
Do you have any recommendations as to how to make predictions with a trained model?
model.predict() expects the first parameter to be a numpy array. You supply a list, which does not have the shape attribute a numpy array has.
Otherwise your code looks fine, except that you are doing nothing with the prediction. Make sure you store it in a variable, for example like this:
prediction = model.predict(np.array(tk.texts_to_sequences(text)))
print(prediction)
model.predict_classes(<numpy_array>)
Sample https://gist.github.com/alexcpn/0683bb940cae510cf84d5976c1652abd
You can just "call" your model with an array of the correct shape:
model(np.array([[6.7, 3.3, 5.7, 2.5]]))
Full example:
from sklearn.datasets import load_iris
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
import numpy as np
X, y = load_iris(return_X_y=True)
model = Sequential([
Dense(16, activation='relu'),
Dense(32, activation='relu'),
Dense(1)])
model.compile(loss='mean_absolute_error', optimizer='adam')
history = model.fit(X, y, epochs=10, verbose=0)
print(model(np.array([[6.7, 3.3, 5.7, 2.5]])))
<tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[1.92517677]])>
You must use the same Tokenizer you used to build your model!
Else this will give different vector to each word.
Then, I am using:
phrase = "not good"
tokens = myTokenizer.texts_to_matrix([phrase])
model.predict(np.array(tokens))
Your can use your tokenizer and pad sequencing for a new piece of text. This is followed by model prediction. This will return the prediction as a numpy array plus the label itself.
For example:
new_complaint = ['Your service is not good']
seq = tokenizer.texts_to_sequences(new_complaint)
padded = pad_sequences(seq, maxlen=maxlen)
pred = model.predict(padded)
print(pred, labels[np.argmax(pred)])
I trained a neural network in Keras to perform non linear regression on some data. This is some part of my code for testing on new data using previously saved model configuration and weights.
fname = r"C:\Users\tauseef\Desktop\keras\tutorials\BestWeights.hdf5"
modelConfig = joblib.load('modelConfig.pkl')
recreatedModel = Sequential.from_config(modelConfig)
recreatedModel.load_weights(fname)
unseenTestData = np.genfromtxt(r"C:\Users\tauseef\Desktop\keras\arrayOf100Rows257Columns.txt",delimiter=" ")
X_test = unseenTestData
standard_scalerX = StandardScaler()
standard_scalerX.fit(X_test)
X_test_std = standard_scalerX.transform(X_test)
X_test_std = X_test_std.astype('float32')
unseenData_predictions = recreatedModel.predict(X_test_std)
Follow WeChat
Success story sharing
Want to stay one step ahead of the latest teleworks?
Subscribe Now相似问题
- How do I merge two dictionaries in a single expression?
- How do I check whether a file exists without exceptions?
- How do I execute a program or call a system command?
- How can I safely create a nested directory?
- How do I make a flat list out of a list of lists?
- How do I clone a list so that it doesn't change unexpectedly after assignment?
fit().[[0.49334425], [0.4927475 ]]. Can you please tell me how should I interpret this!(2, 1)which indicates to me that your model outputs one value and you passed in two input vectors. This is all I can say. For future questions please open a new question with more details and a specific problem!