<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Using QKeras
QKeras works by tagging all variables and weights/bias created by Keras as well as output of arithmetic layers by quantized functions. Quantized functions can be instantiated directly in QSimpleRNN/QLSTM/QGRU/QBidirectional/QDense/QConv2D/QSeparableConv2D functions, and they can be passed to QActivation, which act as a merged quantization and activation function.
In order to successfully quantize a model, users need to replace layers that create variables (trainable or not) (LSTM, Conv2D, etc) by their equivalent ones in QKeras (QLSTM/QDense, etc), and any layers that perform math operations need to be quantized afterwards.
Quantized values are clipped between their maximum and minimum quantized representation (which may be different than $[-1.0, 1.0]$), although for po2 type of quantizers, we still recommend the users to specify the parameter for max_value.
An example of a very simple recurrent network is given below in Keras.
Step1:  Replacing with quantized layers
Step2:  Converting a Model Automatically
In addition to the drop-in replacement of Keras functions, we have written the following function to assist anyone who wants to quantize a network.
model_quantize(model, quantizer_config, activation_bits, custom_objects=None, transfer_weights=False)
This function converts an non-quantized model (such as the one from model in the previous example) into a quantized version, by applying a configuration specified by the dictionary quantizer_config, and activation_bits specified for unamed activation functions, with this parameter probably being removed in future versions.
The parameter custom_objects specifies object dictionary unknown to Keras, required when you copy a model with lambda layers, or customized layer functions, for example, and if transfer_weights is True, the returned model will have as initial weights the weights from the original model, instead of using random initial weights.
The dictionary specified in quantizer_config can be indexed by a layer name or layer class name. In the example below, conv2d_1 corresponds to the first convolutional layer of the example, while  QConv2D corresponds to the default behavior of two dimensional convolutional layers. The reader should note that right now we recommend using QActivation with a dictionary to avoid the conversion of activations such as softmax and linear.  In addition, although we could use activation field in the layers, we do not recommend that. 
{
  "conv2d_1"
Step3:  Quantizing a Model With AutoQKeras
To quantize this model with AutoQKeras, we need to define the quantization for kernels, biases and activations; forgiving factors and quantization strategy.
Below we define which quantizers are allowed for kernel, bias, activations and linear. Linear is a proxy that we use to capture Activation("linear") to apply quantization without applying a non-linear operation.  In some networks, we found that this trick may be necessary to better represent the quantization space.
<ASSISTANT_TASK:>
Python Code:
units = 64
embedding_dim = 64
loss = 'binary_crossentropy'
def create_model(batch_size=None):
  x = x_in = Input(shape=(maxlen,), batch_size=batch_size, dtype=tf.int32)
  x = Embedding(input_dim=max_features, output_dim=embedding_dim)(x)
  x = Activation('linear', name='embedding_act')(x)
  x = Bidirectional(LSTM(units))(x)
  x = Dense(1)(x)
  x = Activation('sigmoid')(x)
  model = tf.keras.Model(inputs=[x_in], outputs=[x])
  return model
tf.keras.backend.clear_session()
with strategy.scope():
  model = create_model(BATCH_SIZE)
  custom_objects = {}
  model.compile(
      optimizer=Adam(learning_rate=0.01),
      loss=loss,
      metrics=['acc'])
model.summary()
print('Train...')
model.fit(
    train_dataset,
    epochs=10,
    batch_size=BATCH_SIZE,
    validation_data=test_dataset,
    verbose=2)
Explanation: Using QKeras
QKeras works by tagging all variables and weights/bias created by Keras as well as output of arithmetic layers by quantized functions. Quantized functions can be instantiated directly in QSimpleRNN/QLSTM/QGRU/QBidirectional/QDense/QConv2D/QSeparableConv2D functions, and they can be passed to QActivation, which act as a merged quantization and activation function.
In order to successfully quantize a model, users need to replace layers that create variables (trainable or not) (LSTM, Conv2D, etc) by their equivalent ones in QKeras (QLSTM/QDense, etc), and any layers that perform math operations need to be quantized afterwards.
Quantized values are clipped between their maximum and minimum quantized representation (which may be different than $[-1.0, 1.0]$), although for po2 type of quantizers, we still recommend the users to specify the parameter for max_value.
An example of a very simple recurrent network is given below in Keras.
End of explanation
def create_qmodel(batch_size=None):
  x = x_in = Input(shape=(maxlen,), batch_size=batch_size, dtype=tf.int32)
  x = Embedding(input_dim=max_features, output_dim=embedding_dim)(x)
  x = QActivation('binary', name='embedding_act')(x)
  x = QLSTM(
    units,
    activation='quantized_tanh(4)',
    recurrent_activation='quantized_relu(4,0,1)',
    kernel_quantizer='stochastic_ternary("auto")',
    recurrent_quantizer='quantized_bits(2,1,1,alpha=1.0)',
    bias_quantizer='quantized_bits(4,0,1)')(x)
  x = QDense(
    1, 
    kernel_quantizer="quantized_bits(4,0,1)",
    bias_quantizer='quantized_bits(4,0,1)')(x)
  x = QActivation('sigmoid')(x)
  model = tf.keras.Model(inputs=[x_in], outputs=[x])
  return model
tf.keras.backend.clear_session()
with strategy.scope():
  qmodel = create_qmodel(BATCH_SIZE)
  custom_objects = {}
  qmodel.compile(
      optimizer=Adam(learning_rate=0.01),
      loss=loss,
      metrics=['acc'])
qmodel.summary()
print('Train...')
qmodel.fit(train_dataset,
          batch_size=BATCH_SIZE,
          epochs=10,
          verbose=2,
          validation_data=test_dataset)
Explanation: Replacing with quantized layers
End of explanation
bits = 4
quantizer_config = {
  "bidirectional": {
      'activation' : f"quantized_tanh({bits})",
      'recurrent_activation' : f"quantized_relu(4,0,1)",
      'kernel_quantizer' : f"quantized_bits({bits}, alpha='auto')",
      'recurrent_quantizer' : f"quantized_bits({bits}, alpha='auto')",
      'bias_quantizer' : f"quantized_bits({bits}, alpha='auto')",
  },
  "dense": {
      'kernel_quantizer' : f"quantized_bits({bits}), alpha='auto'",
      'bias_quantizer' : f"quantized_bits({bits}), alpha='auto'"
  },
  "embedding_act": f"quantized_bits({bits}), alpha='auto'",
}
tf.keras.backend.clear_session()
with strategy.scope():
  model = create_model(BATCH_SIZE)
  custom_objects = {}
  
  qmodel = model_quantize(model, quantizer_config, bits, custom_objects)
  qmodel.compile(
      optimizer=Adam(learning_rate=0.01),
      loss=loss,
      metrics=['acc'])
  
qmodel.summary()
print('Train...')
qmodel.fit(train_dataset,
          batch_size=BATCH_SIZE,
          epochs=10,
          verbose=2,
          validation_data=test_dataset)
Explanation: Converting a Model Automatically
In addition to the drop-in replacement of Keras functions, we have written the following function to assist anyone who wants to quantize a network.
model_quantize(model, quantizer_config, activation_bits, custom_objects=None, transfer_weights=False)
This function converts an non-quantized model (such as the one from model in the previous example) into a quantized version, by applying a configuration specified by the dictionary quantizer_config, and activation_bits specified for unamed activation functions, with this parameter probably being removed in future versions.
The parameter custom_objects specifies object dictionary unknown to Keras, required when you copy a model with lambda layers, or customized layer functions, for example, and if transfer_weights is True, the returned model will have as initial weights the weights from the original model, instead of using random initial weights.
The dictionary specified in quantizer_config can be indexed by a layer name or layer class name. In the example below, conv2d_1 corresponds to the first convolutional layer of the example, while  QConv2D corresponds to the default behavior of two dimensional convolutional layers. The reader should note that right now we recommend using QActivation with a dictionary to avoid the conversion of activations such as softmax and linear.  In addition, although we could use activation field in the layers, we do not recommend that. 
{
  "conv2d_1": {
      "kernel_quantizer": "stochastic_ternary",
      "bias_quantizer": "quantized_po2(4)"
  },
  "QConv2D": {
      "kernel_quantizer": "stochastic_ternary",
      "bias_quantizer": "quantized_po2(4)"
  },
  "QDense": {
      "kernel_quantizer": "quantized_bits(3,0,1)",
      "bias_quantizer": "quantized_bits(3)"
  },
  "act_1": "quantized_relu(2)",
  "QActivation": { "relu": "quantized_relu(2)" }
}
In the following example, we will quantize the model using a different strategy.
End of explanation
tf.keras.backend.clear_session()
with strategy.scope():
  model = create_model(BATCH_SIZE)
  custom_objects = {}
  model.compile(
      optimizer=Adam(learning_rate=0.01),
      loss=loss,
      metrics=['acc'])
quantization_config = {
        "kernel": {
                "stochastic_binary": 1,
                "stochastic_ternary": 2,
                "quantized_bits(4,0,1,alpha=1.0)": 4,
                "quantized_po2(4,1)": 4
        },
        "recurrent_kernel": {
                "stochastic_binary": 1,
                "stochastic_ternary": 2,
                "quantized_bits(4,0,1,alpha=1.0)": 4,
                "quantized_po2(4,1)": 4
          
        },
        "recurrent_activation": {
                "quantized_relu(4,0,1)": 4          
        },
        "bias": {
                "quantized_bits(4,0,1)": 4,
                "quantized_po2(4,1)": 4
        },
        "activation" : {
            "stochastic_ternary('auto')": 2,
            "quantized_tanh(4)" : 4, 
            "quantized_relu_po2(4,1)": 4,
            "quantized_relu(4,2)": 4,
        },
        "linear": { 
                "stochastic_ternary('auto')" : 2,
                "quantized_tanh(4)" : 4, 
                "quantized_relu_po2(4,1)": 4,
                "quantized_relu(3,1)": 3,
                "quantized_relu(4,2)": 4,
        }
}
limit = {
    "Dense": [4],
    "Bidirectional": [4],
    "Activation": [4],
    "default" : [4]*4
}
goal = {
    "type": "bits",
    "params": {
        "delta_p": 8.0,
        "delta_n": 8.0,
        "rate": 2.0,
        "stress": 1.0,
        "input_bits": 4,
        "output_bits": 4,
        "ref_bits": 4,
        "config": {
            "default": ["parameters", "activations"]
        }
    }
}
run_config = {
  "output_dir": tempfile.mkdtemp(),
  "goal": goal,
  "quantization_config": quantization_config,
  "learning_rate_optimizer": False,
  "transfer_weights": False,
  "mode": "random",
  "seed": 42,
  "limit": limit,
  "tune_filters": "layer",
  "tune_filters_exceptions": "^dense",
  "distribution_strategy": strategy,
  "layer_indexes": range(2, len(model.layers) - 1),
  "max_trials": 1000
}
print("quantizing layers:", [model.layers[i].name for i in run_config["layer_indexes"]])
autoqk = AutoQKeras(model, metrics=["acc"], custom_objects={}, **run_config)
autoqk.fit(
  train_dataset, 
  validation_data=test_dataset, 
  batch_size=BATCH_SIZE, 
  epochs=10,
  verbose=2)
qmodel = autoqk.get_best_model()
qmodel.save_weights("qmodel.h5")
print_qmodel_summary(qmodel)
print(get_quantization_dictionary(qmodel))
Explanation: Quantizing a Model With AutoQKeras
To quantize this model with AutoQKeras, we need to define the quantization for kernels, biases and activations; forgiving factors and quantization strategy.
Below we define which quantizers are allowed for kernel, bias, activations and linear. Linear is a proxy that we use to capture Activation("linear") to apply quantization without applying a non-linear operation.  In some networks, we found that this trick may be necessary to better represent the quantization space.
End of explanation
<END_TASK>