model save /load not working properly

[pianoman4873]

I have the following code -

def BuildMatrixFactorizationModel(usersCount, qaPairsCount): left = Sequential() left.add(Embedding(usersCount, EMBEDDING_DIM, input_length=1)) left.add(Reshape((EMBEDDING_DIM,)))

right = Sequential()
right.add(Embedding(qaPairsCount, EMBEDDING_DIM, input_length=1))
right.add(Reshape((EMBEDDING_DIM,)))

model = Sequential()
model.add( Merge ([left, right], mode='dot'))
model.compile(loss='mse', optimizer='adamax' , metrics= ['acc'] )
return model

if name == 'main': path = "e:/model.h5" model = BuildMatrixFactorizationModel(100000,5000) model.save(path) del model model = load_model(path)

However , load_model fails with the error message -

File "C:\Users\pianoman\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\topology.py", line 2877, in load_weights_from_hdf5_group str(len(filtered_layers)) + ' layers.') ValueError: You are trying to load a weight file containing 2 layers into a model with 0 layers.

Any idea what is wrong ?

Note that I was able to train the model - only loading of the presisted model fails.

[dhilton]

I had to reformat your code so please correct it if I've made a mistake:

from keras.models import Sequential
from keras.layers import Embedding
from keras.layers.core import Reshape
from keras.layers import Merge
from keras.models import load_model

EMBEDDING_DIM=25

def BuildMatrixFactorizationModel(usersCount, qaPairsCount):
    left = Sequential()
    left.add(Embedding(usersCount, EMBEDDING_DIM, input_length=1))
    left.add(Reshape((EMBEDDING_DIM,)))

    right = Sequential()
    right.add(Embedding(qaPairsCount, EMBEDDING_DIM, input_length=1))
    right.add(Reshape((EMBEDDING_DIM,)))

    model = Sequential()
    model.add( Merge ([left, right], mode='dot'))
    model.compile(loss='mse', optimizer='adamax' , metrics= ['acc'] )
    return model

path = "model.h5"
model = BuildMatrixFactorizationModel(100000,5000)
model.save(path)
del model
model = load_model(path)

The error you're seeing is because your model has only one layer that has no weights. The load_model function calls in the function load_weights_from_hd5_group a test that each layer has a weight and does an equivalence check between the layers with weights and all layers in your model.

If you look at the abstract base type of a layer (https://github.com/fchollet/keras/blob/master/keras/engine/topology.py#L179 ) a layer has to implement a get_weights and a set_weights method.

I believe that your issue is related to #3927 - for some reason the merge layer has no weights. But the merged layers (left & right in your example) do have weights which is why the error message references two layers. Sorry I couldn't help more.

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[VjunetXuuftofi]

Sorry to reopen this, but I wanted to know if there has been any change or if anybody has any suggestions. I'm having a similar issue with a different (but also nonstandard) architecture. My model:

lstm_dim = 400
output_dim = max_length

main_input = Input((None, 3), name='main_input')
main_input_masked = Masking(mask_value=-1)(main_input)

hidden_input = Input(shape=(1,), name='hidden_input')

hidden_state = Dense(lstm_dim)(hidden_input)
hidden_state = LeakyReLU()(hidden_state)

c_state = Dense(lstm_dim)(hidden_input)
c_state = LeakyReLU()(c_state)

lstm_encoder, hidden, c = LSTM(lstm_dim, return_state=True, implementation=2)(main_input_masked, 
                                                                              initial_state=[hidden_state,
                                                                                             c_state])
output = Dense(2, activation="softmax")(lstm_encoder)

model = Model(inputs=[main_input, hidden_input], outputs=[output])

model.compile(loss="categorical_crossentropy", optimizer=Adam(lr=0.002), metrics ["binary_accuracy"])

Model compiles, fits, and predicts fine. It appears to save fine using model.save(). But when loaded, the following error occurs:

ValueError: Layer lstm_1 expects 1 inputs, but it received 3 input tensors. Input received: [<tf.Tensor 'masking_1_2/mul:0' shape=(?, ?, 3) dtype=float32>, <tf.Tensor 'leaky_re_lu_1_2/sub:0' shape=(?, 400) dtype=float32>, <tf.Tensor 'leaky_re_lu_2_2/sub:0' shape=(?, 400) dtype=float32>]

I tried saving the architecture and the weights separately, but loading the architecture from json did not work. The architecture looked like this:

{"class_name": "Model", "config": {"name": "model_1", "layers": [{"name": "hidden_input", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 1], "dtype": "float32", "sparse": false, "name": "hidden_input"}, "inbound_nodes": []}, {"name": "main_input", "class_name": "InputLayer", "config": {"batch_input_shape": [null, null, 3], "dtype": "float32", "sparse": false, "name": "main_input"}, "inbound_nodes": []}, {"name": "dense_1", "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "units": 400, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["hidden_input", 0, 0, {}]]]}, {"name": "dense_2", "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 400, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["hidden_input", 0, 0, {}]]]}, {"name": "masking_1", "class_name": "Masking", "config": {"name": "masking_1", "trainable": true, "mask_value": -1}, "inbound_nodes": [[["main_input", 0, 0, {}]]]}, {"name": "leaky_re_lu_1", "class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_1", "trainable": true, "alpha": 0.30000001192092896}, "inbound_nodes": [[["dense_1", 0, 0, {}]]]}, {"name": "leaky_re_lu_2", "class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_2", "trainable": true, "alpha": 0.30000001192092896}, "inbound_nodes": [[["dense_2", 0, 0, {}]]]}, {"name": "lstm_1", "class_name": "LSTM", "config": {"name": "lstm_1", "trainable": true, "return_sequences": false, "return_state": true, "go_backwards": false, "stateful": false, "unroll": false, "implementation": 2, "units": 400, "activation": "tanh", "recurrent_activation": "hard_sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0}, "inbound_nodes": [[["masking_1", 0, 0, {}], ["leaky_re_lu_1", 0, 0, {}], ["leaky_re_lu_2", 0, 0, {}]]]}, {"name": "dense_3", "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "units": 2, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["lstm_1", 0, 0, {}]]]}], "input_layers": [["main_input", 0, 0], ["hidden_input", 0, 0]], "output_layers": [["dense_3", 0, 0]]}, "keras_version": "2.0.6", "backend": "tensorflow"}

Is there any workaround for this issue, or do I have to implement this kind of architecture directly in TensorFlow?

[phimit]

exact same problem here, with Keras 2.0.6 and TF 1.3 here's the general structure of the model, basically a merge of 2 LSTMs

model1 = Sequential() model1.add(embed1) model1.add(rnn_shared) model1.add(BatchNormalization())

model2 = Sequential() model2.add(embed2) model2.add(rnn_shared) model2.add(BatchNormalization())

model = Sequential() model.add(Merge([model1,model2],mode="concat")) model.add(Dropout(params["dropout"])) model.add(Dense(1, activation='sigmoid'))

[genimind]

Keras 2.0.8 release includes a fix to a similar issue in regard to loading models with RNN that include initial states.