  In order to leverage the full power of quantum noise squeezing with
unavoidable decoherence, a complete understanding of the degradation in the
purity of squeezed light is demanded. By implementing machine learning
architecture with a convolutional neural network, we illustrate a fast, robust,
and precise quantum state tomography for continuous variables, through the
experimentally measured data generated from the balanced homodyne detectors.
Compared with the maximum likelihood estimation method, which suffers from
time-consuming and over-fitting problems, a well-trained machine fed with
squeezed vacuum and squeezed thermal states can complete the task of
reconstruction of the density matrix in less than one second. Moreover, the
resulting fidelity remains as high as $0.99$ even when the anti-squeezing level
is higher than $20$~dB. Compared with the phase noise and loss mechanisms
coupled from the environment and surrounding vacuum, experimentally, the
degradation information is unveiled with machine learning for low and high
noisy scenarios, i.e., with the anti-squeezing levels at $12$~dB and $18$~dB,
respectively. Our neural network enhanced quantum state tomography provides the
metrics to give physical descriptions of every feature observed in the quantum
state with a single-shot measurement and paves a way of exploring large-scale
quantum systems in real-time.
