USER
import sys
import os
import pennylane as qml
from pennylane import numpy as np
import tensorflow as tf
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from tensorflow.python.keras.layers import Dense
# Redirect stdout to capture print outputs
class Tee(object):
def __init__(self, *files):
self.files = files
def write(self, obj):
for f in self.files:
f.write(obj)
f.flush()
def flush(self):
for f in self.files:
f.flush()
output_file = open('output.log', 'w', encoding='utf-8')
original_stdout = sys.stdout
sys.stdout = Tee(sys.stdout, output_file)
def save_plot(fig, filename):
fig.savefig(filename)
plt.close(fig)
def plot_history(history):
# Plot training and validation accuracy
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(history.history['accuracy'])
ax.plot(history.history['val_accuracy'])
ax.set_title('Model Accuracy')
ax.set_ylabel('Accuracy')
ax.set_xlabel('Epoch')
ax.legend(['Train', 'Validation'], loc='upper left')
save_plot(fig, 'accuracy_plot.png')
# Plot training and validation loss
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(history.history['loss'])
ax.plot(history.history['val_loss'])
ax.set_title('Model Loss')
ax.set_ylabel('Loss')
ax.set_xlabel('Epoch')
ax.legend(['Train', 'Validation'], loc='upper left')
save_plot(fig, 'loss_plot.png')
# Define the quantum device with 11 qubits
dev = qml.device("default.qubit", wires=11)
@qml.qnode(dev, interface='tf', diff_method='backprop')
def quantum_neural_network(inputs, encoding_weights, rot_weights):
control_qubits = [0, 1]
num_layers = encoding_weights.shape[0]
# Define quantum qubit indices
data_qubits_per_layer = []
swap_control_qubits = []
qubit_idx = 2 # Start from the 3rd qubit
for layer in range(num_layers):
data_qubits = [qubit_idx, qubit_idx + 1] # 2 qubits per layer
qubit_idx += 2
data_qubits_per_layer.append(data_qubits)
swap_control_qubit = qubit_idx
swap_control_qubits.append(swap_control_qubit)
qubit_idx += 1 # Add 1 for the swap control qubit
# Process each layer
for layer_idx, data_qubits in enumerate(data_qubits_per_layer):
swap_control_qubit = swap_control_qubits[layer_idx]
# Encode each feature
for cv_idx, cv in enumerate([[0, 0], [0, 1], [1, 0], [1, 1]]):
feature_value = inputs[cv_idx]
angle_weight = encoding_weights[layer_idx, cv_idx]
# Flip control qubits
for qubit, val in zip(control_qubits, cv):
if val == 0:
qml.PauliX(wires=qubit)
# Apply controlled RY gates with feature values and weights
for data_qubit in data_qubits:
qml.ctrl(qml.RY, control=control_qubits)(feature_value, wires=data_qubit)
qml.ctrl(qml.RY, control=control_qubits)(angle_weight, wires=data_qubit)
# Restore control qubits
for qubit, val in zip(control_qubits, cv):
if val == 0:
qml.PauliX(wires=qubit)
# Apply swap gate
qml.Hadamard(wires=swap_control_qubit)
qml.CSWAP(wires=[swap_control_qubit, data_qubits[0], data_qubits[1]])
qml.Hadamard(wires=swap_control_qubit)
# Apply rotation layers
total_qubits = qubit_idx
for layer in range(rot_weights.shape[0]):
for qubit in range(total_qubits):
rot_angles = rot_weights[layer, qubit, :]
qml.Rot(rot_angles[0], rot_angles[1], rot_angles[2], wires=qubit)
# Measure swap control qubits
results = []
for swap_control_qubit in swap_control_qubits:
results.append(qml.expval(qml.PauliZ(wires=swap_control_qubit)))
return results
# Define a quantum neural network layer
class QuantumNeuralNetworkLayer(tf.keras.layers.Layer):
def __init__(self, n_layers, n_features=4, **kwargs):
super(QuantumNeuralNetworkLayer, self).__init__(**kwargs)
self.n_layers = n_layers
self.n_features = n_features
# Define encoding weights
self.encoding_weights = self.add_weight(
shape=(n_layers, n_features),
initializer=tf.keras.initializers.GlorotUniform(),
trainable=True,
name='encoding_weights',
dtype=tf.float64
)
# Calculate total qubits = 2 control qubits + n_layers * (2 data qubits + 1 swap control qubit)
total_qubits = 2 + n_layers * (2 + 1)
self.rot_weights = self.add_weight(
shape=(n_layers, total_qubits, 3),
initializer=tf.keras.initializers.GlorotUniform(),
trainable=True,
name='rot_weights',
dtype=tf.float64
)
def call(self, inputs):
def apply_qnode(x):
x = tf.cast(x, dtype=tf.float64)
result = quantum_neural_network(x, self.encoding_weights, self.rot_weights)
return tf.cast(result, dtype=tf.float32)
# Reshape inputs to be compatible with tf.map_fn
inputs = tf.reshape(inputs, (-1, self.n_features))
outputs = tf.map_fn(
apply_qnode,
inputs,
fn_output_signature=tf.float32
)
return tf.reshape(outputs, (-1, self.n_layers))
# Define a deep quantum neural network model
class DeepQuantumNeuralNetwork(tf.keras.Model):
def __init__(self, n_quantum_layers, n_layers_per_qnn, num_classes, n_features=4, **kwargs):
super(DeepQuantumNeuralNetwork, self).__init__(**kwargs)
self.n_quantum_layers = n_quantum_layers
# Stack multiple quantum layers
self.quantum_layers = [
QuantumNeuralNetworkLayer(n_layers=n_layers_per_qnn, n_features=n_features)
for _ in range(n_quantum_layers)
]
# Final classical dense layer
self.dense = Dense(num_classes, activation='softmax')
def call(self, inputs):
# Pass through each quantum layer
x = inputs
for layer in self.quantum_layers:
x = layer(x)
# Apply the final dense layer
return self.dense(x)
# Create an instance of the deep quantum neural network
n_quantum_layers = 3 # Number of quantum layers to stack
n_layers_per_qnn = 2 # Number of rotation layers per quantum neural network
model = DeepQuantumNeuralNetwork(
n_quantum_layers=n_quantum_layers,
n_layers_per_qnn=n_layers_per_qnn,
num_classes=3
)
# Compile and train the model
learning_rate = 0.1
epochs = 100
batch_size = 8
model.compile(
optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate),
loss='categorical_crossentropy',
metrics=['accuracy']
)
# Load and preprocess data
iris = load_iris()
X = iris['data'].astype(np.float32)
y = iris['target'].reshape(-1, 1)
# One-hot encode the labels
encoder = OneHotEncoder(sparse_output=False)
y = encoder.fit_transform(y).astype(np.float32)
# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=116
)
# Train the model
history = model.fit(
x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test)
)
# Evaluate the model
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
plot_history(history)
# Close the output file and restore stdout
sys.stdout = original_stdout
output_file.close()
print("脚本执行完成。查看 'output.log' 获取打印输出，查看当前目录获取保存的图表。")
代码出现报错Traceback (most recent call last):
File "G:\Hellyc\yuanweihua-NewQNN-default-layers=4_4feature _AngCode\examples\yuanweihua_renew_usesoftmax_NQNN_default_4feature_AngCode_Layers=5.py", line 223, in <module>
history = model.fit(
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "G:\anaconda3\envs\pennylane2\lib\site-packages\tensorflow\python\eager\execute.py", line 54, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'categorical_crossentropy/softmax_cross_entropy_with_logits' defined at (most recent call last):
File "G:\Hellyc\yuanweihua-NewQNN-default-layers=4_4feature _AngCode\examples\yuanweihua_renew_usesoftmax_NQNN_default_4feature_AngCode_Layers=5.py", line 223, in <module>
history = model.fit(
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 1564, in fit
tmp_logs = self.train_function(iterator)
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 1160, in train_function
return step_function(self, iterator)
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 1146, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 1135, in run_step
outputs = model.train_step(data)
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 994, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 1052, in compute_loss
return self.compiled_loss(
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\compile_utils.py", line 265, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\losses.py", line 152, in __call__
losses = call_fn(y_true, y_pred)
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\losses.py", line 272, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\losses.py", line 1990, in categorical_crossentropy
return backend.categorical_crossentropy(
File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\backend.py", line 5535, in categorical_crossentropy
return tf.nn.softmax_cross_entropy_with_logits(
Node: 'categorical_crossentropy/softmax_cross_entropy_with_logits'
logits and labels must be broadcastable: logits_size=[2,3] labels_size=[8,3]
[[{{node categorical_crossentropy/softmax_cross_entropy_with_logits}}]] [Op:__inference_train_function_251364]
如何进行解决
给出修改后的完整代码ASSISTANT
The error you’re encountering:
```
InvalidArgumentError: Graph execution error:
Detected at node 'categorical_crossentropy/softmax_cross_entropy_with_logits':
logits and labels must be broadcastable: logits_size=[2,3] labels_size=[8,3]
```
indicates a mismatch between the batch sizes of your model's outputs (logits) and your labels. Specifically, the logits have a shape of `[2, 3]`, while the labels have a shape of `[8, 3]`. This suggests that the model is producing outputs for only 2 samples when it should be processing 8 samples per batch.
### Likely Cause
The issue arises from how the `QuantumNeuralNetworkLayer` processes the inputs and reshapes the outputs. Specifically, the `tf.reshape` operation within the `QuantumNeuralNetworkLayer` might be altering the intended batch size, leading to the mismatch.
### Solution
1. **Remove Unnecessary Reshape:**
The `tf.reshape` operation in the `QuantumNeuralNetworkLayer`'s `call` method can inadvertently alter the batch size. Removing this reshape ensures that the batch size remains consistent throughout the model.
2. **Adjust `n_features` for Subsequent Quantum Layers:**
When stacking multiple quantum layers, ensure that the number of input features (`n_features`) for each subsequent quantum layer matches the output features of the previous layer. For instance, if the first quantum layer outputs 2 features, the next quantum layer should expect 2 features as input.
### Corrected Code
Below is the complete modified code with the necessary corrections applied:
```python
import sys
import os
import pennylane as qml
from pennylane import numpy as np
import tensorflow as tf
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from tensorflow.python.keras.layers import Dense
# Redirect stdout to capture print outputs
class Tee(object):
def __init__(self, *files):
self.files = files
def write(self, obj):
for f in self.files:
f.write(obj)
f.flush()
def flush(self):
for f in self.files:
f.flush()
output_file = open('output.log', 'w', encoding='utf-8')
original_stdout = sys.stdout
sys.stdout = Tee(sys.stdout, output_file)
def save_plot(fig, filename):
fig.savefig(filename)
plt.close(fig)
def plot_history(history):
# Plot training and validation accuracy
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(history.history['accuracy'])
ax.plot(history.history['val_accuracy'])
ax.set_title('Model Accuracy')
ax.set_ylabel('Accuracy')
ax.set_xlabel('Epoch')
ax.legend(['Train', 'Validation'], loc='upper left')
save_plot(fig, 'accuracy_plot.png')
# Plot training and validation loss
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(history.history['loss'])
ax.plot(history.history['val_loss'])
ax.set_title('Model Loss')
ax.set_ylabel('Loss')
ax.set_xlabel('Epoch')
ax.legend(['Train', 'Validation'], loc='upper left')
save_plot(fig, 'loss_plot.png')
# Define the quantum device with 11 qubits
dev = qml.device("default.qubit", wires=11)
@qml.qnode(dev, interface='tf', diff_method='backprop')
def quantum_neural_network(inputs, encoding_weights, rot_weights):
control_qubits = [0, 1]
num_layers = encoding_weights.shape[0]
# Define quantum qubit indices
data_qubits_per_layer = []
swap_control_qubits = []
qubit_idx = 2 # Start from the 3rd qubit
for layer in range(num_layers):
data_qubits = [qubit_idx, qubit_idx + 1] # 2 qubits per layer
qubit_idx += 2
data_qubits_per_layer.append(data_qubits)
swap_control_qubit = qubit_idx
swap_control_qubits.append(swap_control_qubit)
qubit_idx += 1 # Add 1 for the swap control qubit
# Process each layer
for layer_idx, data_qubits in enumerate(data_qubits_per_layer):
swap_control_qubit = swap_control_qubits[layer_idx]
# Encode each feature
for cv_idx, cv in enumerate([[0, 0], [0, 1], [1, 0], [1, 1]]):
feature_value = inputs[cv_idx]
angle_weight = encoding_weights[layer_idx, cv_idx]
# Flip control qubits
for qubit, val in zip(control_qubits, cv):
if val == 0:
qml.PauliX(wires=qubit)
# Apply controlled RY gates with feature values and weights
for data_qubit in data_qubits:
qml.ctrl(qml.RY, control=control_qubits)(feature_value, wires=data_qubit)
qml.ctrl(qml.RY, control=control_qubits)(angle_weight, wires=data_qubit)
# Restore control qubits
for qubit, val in zip(control_qubits, cv):
if val == 0:
qml.PauliX(wires=qubit)
# Apply swap gate
qml.Hadamard(wires=swap_control_qubit)
qml.CSWAP(wires=[swap_control_qubit, data_qubits[0], data_qubits[1]])
qml.Hadamard(wires=swap_control_qubit)
# Apply rotation layers
total_qubits = qubit_idx
for layer in range(rot_weights.shape[0]):
for qubit in range(total_qubits):
rot_angles = rot_weights[layer, qubit, :]
qml.Rot(rot_angles[0], rot_angles[1], rot_angles[2], wires=qubit)
# Measure swap control qubits
results = []
for swap_control_qubit in swap_control_qubits:
results.append(qml.expval(qml.PauliZ(wires=swap_control_qubit)))
return results
# Define a quantum neural network layer
class QuantumNeuralNetworkLayer(tf.keras.layers.Layer):
def __init__(self, n_layers, n_features=4, **kwargs):
super(QuantumNeuralNetworkLayer, self).__init__(**kwargs)
self.n_layers = n_layers
self.n_features = n_features
# Define encoding weights
self.encoding_weights = self.add_weight(
shape=(n_layers, self.n_features),
initializer=tf.keras.initializers.GlorotUniform(),
trainable=True,
name='encoding_weights',
dtype=tf.float64
)
# Calculate total qubits = 2 control qubits + n_layers * (2 data qubits + 1 swap control qubit)
total_qubits = 2 + n_layers * (2 + 1)
self.rot_weights = self.add_weight(
shape=(n_layers, total_qubits, 3),
initializer=tf.keras.initializers.GlorotUniform(),
trainable=True,
name='rot_weights',
dtype=tf.float64
)
def call(self, inputs):
def apply_qnode(x):
x = tf.cast(x, dtype=tf.float64)
result = quantum_neural_network(x, self.encoding_weights, self.rot_weights)
return tf.cast(result, dtype=tf.float32)
# Apply the quantum node to each input sample
outputs = tf.map_fn(
apply_qnode,
inputs,
fn_output_signature=tf.float32
)
# Remove reshape to preserve batch size
return outputs
# Define a deep quantum neural network model
class DeepQuantumNeuralNetwork(tf.keras.Model):
def __init__(self, n_quantum_layers, n_layers_per_qnn, num_classes, n_features=4, **kwargs):
super(DeepQuantumNeuralNetwork, self).__init__(**kwargs)
self.n_quantum_layers = n_quantum_layers
self.quantum_layers = []
for i in range(n_quantum_layers):
if i == 0:
# First layer expects the original number of features
layer_n_features = n_features
else:
# Subsequent layers expect as many features as the number of layers per QNN
layer_n_features = n_layers_per_qnn
self.quantum_layers.append(
QuantumNeuralNetworkLayer(n_layers=n_layers_per_qnn, n_features=layer_n_features)
)
# Final classical dense layer
self.dense = Dense(num_classes, activation='softmax')
def call(self, inputs):
# Pass through each quantum layer
x = inputs
for layer in self.quantum_layers:
x = layer(x)
# Apply the final dense layer
return self.dense(x)
# Create an instance of the deep quantum neural network
n_quantum_layers = 3 # Number of quantum layers to stack
n_layers_per_qnn = 2 # Number of rotation layers per quantum neural network
model = DeepQuantumNeuralNetwork(
n_quantum_layers=n_quantum_layers,
n_layers_per_qnn=n_layers_per_qnn,
num_classes=3
)
# Compile and train the model
learning_rate = 0.1
epochs = 100
batch_size = 8
model.compile(
optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate),
loss='categorical_crossentropy',
metrics=['accuracy']
)
# Load and preprocess data
iris = load_iris()
X = iris['data'].astype(np.float32)
y = iris['target'].reshape(-1, 1)
# One-hot encode the labels
encoder = OneHotEncoder(sparse_output=False)
y = encoder.fit_transform(y).astype(np.float32)
# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=116
)
# Train the model
history = model.fit(
x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test)
)
# Evaluate the model
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
plot_history(history)
# Close the output file and restore stdout
sys.stdout = original_stdout
output_file.close()
print("脚本执行完成。查看 'output.log' 获取打印输出，查看当前目录获取保存的图表。")
```
### Key Changes and Explanations
1. **Removed the `tf.reshape` Operation:**
```python
# Remove reshape to preserve batch size
return outputs
```
By removing the `tf.reshape(outputs, (-1, self.n_layers))`, we ensure that the batch size remains consistent. The `outputs` from `tf.map_fn` already have the correct shape `[batch_size, n_layers]`.
2. **Adjusted `n_features` for Quantum Layers:**
In the `DeepQuantumNeuralNetwork` class, the number of input features for each quantum layer is adjusted based on its position in the stack:
```python
for i in range(n_quantum_layers):
if i == 0:
# First layer expects the original number of features
layer_n_features = n_features
else:
# Subsequent layers expect as many features as the number of layers per QNN
layer_n_features = n_layers_per_qnn
self.quantum_layers.append(
QuantumNeuralNetworkLayer(n_layers=n_layers_per_qnn, n_features=layer_n_features)
)
```
- **First Quantum Layer:** Expects the original number of features (`n_features=4`).
- **Subsequent Quantum Layers:** Expect the number of features equal to `n_layers_per_qnn` (which is 2 in this case).
3. **Ensured Consistent Data Flow:**
- Each `QuantumNeuralNetworkLayer` processes the input and returns outputs with a consistent shape that matches the expected input for the next layer.
- The final `Dense` layer receives outputs with the correct shape `[batch_size, n_layers_per_qnn]` and maps them to the desired number of classes.
### Additional Recommendations
- **Monitor Shapes During Training:**
To further ensure that the shapes are consistent throughout the model, you can add print statements or use TensorFlow’s debugging tools to monitor the shapes of tensors during the forward pass.
- **Adjust Quantum Layer Parameters if Needed:**
Depending on the performance and behavior of the model, you might need to tweak `n_quantum_layers` and `n_layers_per_qnn`.
- **Use TensorFlow’s Debugging Tools:**
Tools like `tf.debugging` can help identify and resolve shape mismatches and other issues during model training.
### Final Notes
After applying these changes, your model should correctly handle batches without mismatches between logits and labels. Ensure that all quantum layers are correctly parameterized and that subsequent layers receive inputs of the expected shape.
If you continue to encounter issues, consider simplifying the model (e.g., reducing the number of quantum layers) to isolate and identify the problem.
---
**Good luck with your quantum neural network training! If you have further questions or run into other issues, feel free to ask.**